Systems and methods for cardiovascular-dynamics correlated imaging

ABSTRACT

A method for cardiovascular-dynamics correlated imaging includes receiving a time series of images of at least a portion of a patient, receiving a time series of cardiovascular data for the patient, evaluating correlation between the time series of images and the time series of cardiovascular data, and determining a property of the at least a portion of a patient, based upon the correlation. A system for cardiovascular-dynamics correlated imaging includes a processing device having: a processor, a memory communicatively coupled therewith, and a correlation module including machine-readable instructions stored in the memory that, when executed by the processor, perform the function of correlating a time series of images of at least a portion of a patient with a time series of cardiovascular data of the patient to determine a property of the at least a portion of a patient.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/328,981 with a § 371(c) date of Jan. 25, 2017, which is a 35U.S.C. § 371 filing of International Application No. PCT/US2015/042306,filed Jul. 27, 2015, which claims the benefit of priority from U.S.Provisional Application Ser. No. 62/028,949 filed Jul. 25, 2014, each ofwhich is incorporated herein by reference in its entirety.

U.S. GOVERNMENT SUPPORT

This invention was made with government support under contract Nos.CA080139 and CA143020 awarded by the National Institutes of Health. Thegovernment has certain rights in the invention.

BACKGROUND

Physicians commonly utilize imaging in the diagnosis, treatment, andmonitoring of medical conditions. Various imaging methods are capable ofdistinguishing between at least some types of materials in a human body.For example, magnetic resonance imaging (MRI) may distinguish betweenbone material, organs, and various forms of soft tissue. Ultrasound maydistinguish an internal organ from surrounding parts of a human body.Contrast imaging is the imaging of a contrast agent delivered to thetissue or material of interest. The contrast agent is administered tothe patient before or during imaging and predominantly binds to oroccupies certain types of materials. These materials are therebyenhanced in the images. Contrast imaging is utilized to image tissuevascularization. In this case, a contrast agent is delivered to thecardiovascular system of the patient. Detection and evaluation of tissuevascularization is used to diagnose and monitor conditions such ashemangioma, vascular anomalies, and cancer.

Notably, vascularization properties of a malignant tumor tend to bedifferent from those of healthy tissue and benign tumors. Thevascularization topology of a malignant breast tumor, as well as theblood flow properties of the vessels, differs from that of healthybreast tissue. The normal breast is vascularized with a well-organizedand regular network of vessels. On the other hand, the vasculaturearound a malignant tumor is of irregular size, shape, and branchingpattern, and lacks the vascular network hierarchy of healthy breasttissue. In addition, individual vessels are compromised. These featuresof a malignant tumor result in, for example, lower blood flow rates thanin healthy tissue, chaotic blood flow around the malignant tumor, anddiffusion of blood plasma into the surrounding tissue.

Tumor vascularization imaging may detect a malignant tumor ordistinguish a benign breast tumor from a malignant breast tumor. Tumorvascularization imaging may also monitor tumor angiogenesis (theformation of new blood-vessels from existing blood vessels) as a tool tostage disease progression or as a tool to monitor disease progressionduring an anti-angiogenic treatment course.

SUMMARY

In an embodiment, a method for cardiovascular-dynamics correlatedimaging includes (a) receiving a time series of images of at least aportion of a patient, (b) receiving a time series of cardiovascular datafor the patient, (c) evaluating correlation between the time series ofimages and the time series of cardiovascular data, and (d) determining aproperty of the at least a portion of a patient, based upon thecorrelation between the time series of images and the time series ofcardiovascular data.

In an embodiment, a system for cardiovascular-dynamics correlatedimaging includes a processing device having (a) a processor, (b) amemory communicatively coupled with the processor, and (c) a correlationmodule including machine-readable instructions stored in the memorythat, when executed by the processor, perform the function ofcorrelating a time series of images of at least a portion of a patientwith a time series of cardiovascular data of the patient to determine aproperty of the at least a portion of a patient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for cardiovascular-dynamics correlatedimaging, according to an embodiment.

FIG. 2 illustrates another system for cardiovascular-dynamics correlatedimaging, according to an embodiment.

FIG. 3 illustrates a method for cardiovascular-dynamics correlatedimaging, according to an embodiment.

FIG. 4 illustrates a method for reconstructing a time series ofelectrical impedance tomography images, according to an embodiment.

FIG. 5 illustrates a method for evaluating correlation between a timeseries of images and a time series of cardiovascular data, wherein thetime series of images and the time series of cardiovascular data arerecorded at different series of time, according to an embodiment.

FIG. 6 illustrates a method for analyzing correlation between a timeseries of images and heartbeat cycles identified from a time series ofcardiovascular data, according to an embodiment.

FIG. 7 illustrates a method for evaluating temporal correlation betweena time series of images and a time series of cardiovascular data,according to an embodiment.

FIG. 8 illustrates a method for evaluating spectral correlation betweena time series of images and a time series of cardiovascular data,according to an embodiment.

FIG. 9 illustrates a system for cardiovascular dynamics correlatedimaging, which utilizes two different imaging devices, according to anembodiment.

FIG. 10 illustrates a method for overlaying, on a time series of images,a correlation-indicating image, indicating correlation between a timeseries of images and a time series of cardiovascular data, according toan embodiment.

FIG. 11 illustrates a method for overlaying at least a portion of a timeseries of first-type images on a time series of second-type images,according to an embodiment.

FIG. 12 illustrates a method for evaluating correlation between a timeseries of EIT images and a time series of pulse-oximeter measurements,according to an embodiment.

FIG. 13 illustrates a system for cardiovascular-dynamics correlatedphantom imaging, according to an embodiment.

FIGS. 14A-14C show exemplary temporal sequences of electrical impedancetomography images of a phantom.

FIGS. 15A and 15B show exemplary temporal sequences and power spectrafor sine wave excitation and square wave excitation in electricalimpedance tomography imaging of a phantom.

FIG. 16 shows tumor characteristics for cancer containing quadrants ofbreasts of a cohort of patients.

FIGS. 17A and 17B show exemplary time series of images a control(normal) and cancer patient, respectively.

FIG. 18 shows temporal and spectral signatures extracted from anexemplary benign quadrant, along with associated oxygen-saturationsignals.

FIG. 19 shows temporal and spectral signatures extracted from anexemplary malignant quadrant, along with associated oxygen-saturationsignals.

FIG. 20 shows, in tabular form, statistics of normal and cancer patientobtained from processing of exemplary quadrant.

FIG. 21 shows mean parameters for exemplary normal and cancer patients.

FIG. 22 shows receiver-operating characteristics for each of theparameters displayed in FIG. 21.

FIG. 23 shows, in tabular form, clinical metrics associated with thereceiver-operating characteristics displayed in FIG. 22.

FIG. 24 shows patient characteristics in an example study.

FIG. 25 shows abnormal cohort characteristics in an example study.

FIGS. 26A-26E show a representative sequence of changing conductivityand its correlation with pulsatile oxygen saturation for a woman with apathologically confirmed inflammatory breast cancer.

FIGS. 27A-27E show results obtained by imaging women with breast cancer.

FIG. 28 shows a correlation coefficient table for an abnormal cohort.

FIGS. 29A and 29B show comparative results for women with and withoutbreast cancer.

DETAILED DESCRIPTION OF THE EMBODIMENTS

FIG. 1 illustrates one exemplary system 100 for cardiovascular-dynamicscorrelated imaging of an area of interest 145 of a patient 140. System100 includes an imaging device 110, a cardiovascular measurement device120, and a processing device 130. Imaging device 110 images area ofinterest 145 to generate a time series of image data 150 thereof. Timeseries of image data 150 may be a time series of images or a time seriesof non-image data from which a time series of images may bereconstructed. For the purpose of the present disclosure “image” refersto a spatial map of a property, which may be in any representation knownin the art including graphical representation and tabular form. Also forthe purpose of the present disclosure, “time series” refers to asequence of data measured at subsequent points in time, wherein the timepoints may be uniformly or non-uniformly spaced from each other in time.Cardiovascular measurement device 120 records at least one signal thatreflects cardiovascular dynamics of patient 140. Cardiovascularmeasurement device 120 thus produces a time series of cardiovasculardata 160.

Imaging device 110 and cardiovascular measurement device 120respectively captures time series of image data 150 and records timeseries of cardiovascular data 160 concurrently. Hence, cardiovasculardynamics apparent in time series of cardiovascular data 160 may becorrelated with temporal variations in time series of image data 150.Time series of image data 150 and time series of cardiovascular data 160may be recorded at identical series of times or at different series oftimes having temporal overlap. In an example, time series of image data150 is captured at a series of times that is substantially defined bythe maximum rate at which image data may be captured by imaging device110, while time series of cardiovascular data 160 is recorded at aseries of times that is substantially defined by the maximum rate atwhich cardiovascular data may be captured by cardiovascular measurementdevice 120. In one embodiment, the maximum rate of cardiovascularmeasurement device 120 exceeds the maximum rate of imaging device 110such that time series of cardiovascular data 160 has higher temporalresolution than time series of image data 150. In another embodiment,the maximum rate of imaging device 110 exceeds the maximum rate ofcardiovascular measurement device 120 such that time series of imagedata 150 has higher temporal resolution than time series ofcardiovascular data 160.

Imaging device 110 and cardiovascular measurement device 120 arecommunicatively coupled with processing device 130 and communicate timeseries of image data 150 and time series of cardiovascular data 160,respectively, to processing device 130.

Processing device 130 processes time series of image data 150 and timeseries of cardiovascular data 160 to evaluate correlation between timeseries of image data 150 and time series of cardiovascular data 160. Inan embodiment, processing device 130 identifies heartbeat cycles fromtime series of cardiovascular data 160 and evaluates correlation betweentime series of image data 150 and the heartbeat cycles. Through suchcorrelation evaluation, system 100 provides enhanced imaging ofvascularized tissue within area of interest 145. Notably, system 100achieves this without use of a contrast agent. Instead, vascularizedtissue is “highlighted” by correlation of images with measurements ofcardiovascular dynamics. Based upon the degree and/or nature ofcorrelation between time series of image data 150 and time series ofcardiovascular data 160, processing device 130 may detect the presenceof vascularized tissue and provide an image thereof. Processing device130 may distinguish between different types of vascularized tissue. Forexample, malignant tumors may exhibit weaker or stronger correlationwith cardiovascular dynamics than healthy vascularized tissue.Therefore, processing device 130 may distinguish between a malignanttumor and healthy vascularized tissue or a benign tumor.

In some use scenarios, results generated by processing device 130provide the basis for patient diagnosis. In one exemplary use scenario,area of interest 145 is an area with a tumor. By virtue of thecorrelation evaluation performed by processing device 130, system 100determines if the tumor is malignant or benign. In another exemplary usescenario, area of interest 145 is an area with a malignant tumor. System100 is employed to monitor disease status, i.e., status of the malignanttumor, for example during treatment with anti-angiogenic drugs.

Imaging device 110 may be any imaging modality that is sensitive tovascularization. In an embodiment, imaging device 110 is an electricalimpedance tomography (EIT) device. In this embodiment, time series ofimage data 150 is a time series of spatial maps of one or moreelectrical impedance related parameters (such as impedance,conductivity, permittivity, resistivity, admittance, and/or associatedspectral parameters) of area of interest 145, or a time series of imagedata from which a time series of spatial maps of the electricalimpedance or conductivity of area of interest 145 may be reconstructed.The electrical conductivity of blood is generally around 0.6Siemens/meter (S/m), while the electrical conductivity of other softtissue generally is in the range between 0.1 S/m and 0.2 S/m. Hence, theelectrical conductivity contrast between blood and other soft tissue isabout 3:1. Accordingly, EIT is sensitive to vascularization. In fact,EIT is more sensitive to vascularization, in terms of contrast betweenvascularized and non-vascularized tissue, than other common imagingmodalities such as magnetic resonance imaging (MRI), computed tomography(CT), positron emission tomography (PET), and ultrasound imaging.Furthermore, EIT devices may image at high speed such that time seriesof image data 150 captures fast vascular dynamics at higher accuracythan slower imaging modalities. In addition, EIT is non-invasive andcheaper than most other imaging modalities. In other embodiments,imaging device 110 is an MRI device, a CT device, a PET device, anultrasonography device, a video endoscope, or a fluoroscope.

Cardiovascular measurement device 120 is, for example, a pulse-oximeter(i.e., a device for measuring blood-oxygen saturation), anelectrocardiograph, a sphygmomanometer, a blood flow measurement device,or another device capable of producing time series of cardiovasculardata 160 at a rate sufficient to capture cardiovascular dynamics. Forexample, cardiovascular measurement device 120 has sensitivity and speedsuch that time series of cardiovascular data 160 includes a signature ofheartbeat cycles.

Cardiovascular measurement device 120 need not be connected to area ofinterest 145, and time series of cardiovascular data 160 need not berepresentative of cardiovascular dynamics specific to area of interest145. It is sufficient that cardiovascular measurement device 120measures cardiovascular dynamics at some location of patient 140. In anembodiment, cardiovascular measurement device 120 records time series ofcardiovascular data 160 at a location different from area of interest145. The distance from area of interest 145 to the location wherecardiovascular measurement device 120 connects to patient 140 may resultin a phase shift between time series of image data 150 and time seriesof cardiovascular data 160. In an example, area of interest 145 is abreast of patient 140, while cardiovascular measurement device 120 is apulse-oximeter connected to a finger of patient 140.

Processing device 130 may evaluate correlation between time series ofimage data 150 and time series of cardiovascular data 160 in real-timeor at any time after receiving time series of image data 150 and timeseries of cardiovascular data 160. Without departing from the scopehereof, certain embodiments of system 100 do not include imaging device110 and/or cardiovascular measurement device 120. In such embodiments,processing device 130 receives time series of image data 150 and/or timeseries of cardiovascular data 160 from devices external to system 100.

FIG. 2 illustrates one exemplary system 200 for cardiovascular-dynamicscorrelated imaging. System 200 is an embodiment of system 100 (FIG. 1).System 200 includes a processing device 210 which is an embodiment ofprocessing device 130 (FIG. 1). Processing device 210 processes timeseries of image data 150 (FIG. 1), associated with area of interest 145(FIG. 1), and time series of cardiovascular data 160 (FIG. 1),associated with patient 140 (FIG. 1), to evaluate correlation betweentime series of image data 150 and time series of cardiovascular data160. In an embodiment, system 200 includes imaging device 110 (FIG. 1)for generating time series of image data 150, and/or cardiovascularmeasurement device 120 (FIG. 1) for recording time series ofcardiovascular data 160.

Processing device 210 includes an interface 220, a processor 230, and amemory 240. Processor 230 is communicatively coupled with interface 220and memory 240. Memory 240 includes a data storage 250, andmachine-readable instructions 260 encoded in a non-volatile portion ofmemory 240. Data storage 250 includes image data storage 252 andcardiovascular data storage 254. Processing device 210 receives timeseries of image data 150 and time series of cardiovascular data 160through interface 220. Processor 230 stores time series of image data150 and time series of cardiovascular data 160 to image data storage 252and cardiovascular data storage 254, respectively. Processor 230processes time series of image data 150 and time series ofcardiovascular data 160 according to instructions 260. Processor 230thus evaluates correlation between time series of image data 150 andtime series of cardiovascular data 160. Processor 230 may also performother processing of time series of image data 150 and time series ofcardiovascular data 160 using instructions 260. Processing device 210may output results of processing by processor 230, such as a correlationevaluation, through interface 220.

Optionally, data storage 250 further includes correlation resultsstorage 258 with optional parameter storage 259. Processor 230 may storeresults of a correlation evaluation of time series of image data 150 andtime series of cardiovascular data 160 to correlation results storage258. Processor 230 may store correlation parameters, such as correlationcoefficients and spectral power ratios to parameter storage 259. Aspectral power ratio is a ratio of power in one spectral range to powerin another spectral range, wherein the two spectral ranges may bepartially overlapping or non-overlapping. Data storage 250 may furtherinclude spatial region of interest (ROI) definitions 256. Spatial ROIdefinitions 256 defines one or more spatial ROIs of time series of imagedata 150, which are considered by processor 230 when evaluatingcorrelation between time series of image data 150 and time series ofcardiovascular data 160. In some embodiments, spatial ROI definitions256 is a fixed property of processing device 210, in which case spatialROI definitions 256 may be located in instructions 260, withoutdeparting from the scope hereof.

In an embodiment, instructions 260 includes correlation instructions 262that has instructions that, upon execution by processor 230, evaluatescorrelation between time series of image data 150 and time series ofcardiovascular data 160. Correlation instructions 262 may include one ormore of resampling instructions 263, signature identificationinstructions 264, spectral correlation instructions 265, and temporalcorrelation instructions 266. Processor 230 may utilize one or more ofthese instructions to evaluate correlation between time series of imagedata 150 and time series of cardiovascular data 160. Optionally,instructions 260 further include image reconstruction instructions 268.Processor 230 may, according to image reconstruction instructions 268,process embodiments of time series of image data 150 that are innon-image form to reconstruct a time series of images. For example,processor 230 reconstructs electrical conductivity images from voltagesor currents measured by an EIT device according to image reconstructioninstructions 268.

In certain embodiments, cardiovascular measurement device 120 iscommunicatively coupled with imaging device 110 such that cardiovascularmeasurement device 120 may control aspects of the operation of imagingdevice 110. For example, capture of image or image data of area ofinterest 145 by imaging device 110 is gated according to vasculardynamics recorded by cardiovascular measurement device 120. Although notillustrated in FIG. 2, such gating may be based upon the detection ofcardiovascular signatures in time series of cardiovascular data 160 byprocessing device 210. Thus, cardiovascular measurement device 120 maybe communicatively coupled with imaging device 110 through processingdevice 210, without departing from the scope hereof.

FIG. 3 illustrates one exemplary method 300 for cardiovascular-dynamicscorrelated imaging. Method 300 is performed by, for example, system 100(FIG. 1) or system 200 (FIG. 2). A step 310 receives a time series ofimages of at least a portion of a patient, such as area of interest 145of patient 140 (FIG. 1), and a time series of cardiovascular data of thepatient. The time series of images, or an underlying time series ofimage data, has been captured concurrently with the recording of thetime series of cardiovascular data. The time series of images, or anunderlying time series of image data, and the time series ofcardiovascular data may be recorded at identical series of times or atdifferent series of times having temporal overlap. Step 310 evaluatescorrelation between the time series of images and the time series ofcardiovascular data. In an example, processor 230 (FIG. 2) retrievestime series of image data 150 (FIGS. 1 and 2), in image form, from imagedata storage 252 (FIG. 2) and time series of cardiovascular data 160(FIGS. 1 and 2) from cardiovascular data storage 254 (FIG. 2), Processor230 executes correlation instructions 262 (FIG. 2) to evaluatecorrelation between time series of image data 150 and time series ofcardiovascular data 160. In an embodiment, one or more smaller spatialROIs within the time series of images are separately considered by step310. For example, processor 230 further retrieves spatial ROIdefinitions from spatial ROI definitions 256 (FIG. 2), and evaluatescorrelation within each of the one or more spatial ROIs according tocorrelation instructions 262.

Optionally, step 310 includes one or both of steps 312 and 314. In step312, spectral correlation between the time series of images and the timeseries of cardiovascular measurements is evaluated. For example,processor 230 executes spectral correlation instructions 265 (FIG. 2) toevaluate spectral correlation between time series of image data 150 andtime series of cardiovascular data 160, i.e. correlation betweenspectral properties of time series of image data 150 and spectralproperties of time series of cardiovascular data 160. Processor 230stores results of the correlation evaluation to correlation resultsstorage 258 and/or outputs the results to a user or an external systemthrough interface 220 (FIG. 2). In step 314, temporal correlationbetween the time series of images and the time series of cardiovascularmeasurements is evaluated. For example, processor 230 executes temporalcorrelation instructions 266 (FIG. 2) to evaluate temporal correlationbetween time series of image data 150 and time series of cardiovasculardata 160.

In an embodiment, step 310 includes a step 316 of evaluating correlationbetween the time series of images and heartbeat cycles identified fromthe time series of cardiovascular data. In an example, processor 230executes signature identification instructions 264 (FIG. 2) on timeseries of cardiovascular data 160 to identify at least one heartbeatcycle. Processor 230 then executes at least a portion of correlationinstructions 262 to evaluate correlation between time series of imagedata 150 and the heartbeat cycle(s).

Steps 312, 314, and 316 may be applied to one or more different spatialROIs within the area of interest of the patient, without departing fromthe scope hereof.

In a step 320, a property of the at least a portion of the patient,represented by the time series of images, is determined from the resultsof the correlation evaluation performed in step 310. In one example, ahigh (or low) degree of correlation, between the time series of imagesand the time series of cardiovascular data, found in step 310 leads tothe identification of healthy vascularized tissue in the at least aportion of the patient represented by the time series of images. Inanother example, a low (or high) degree of correlation, between the timeseries of images and the time series of cardiovascular data, found instep 310 leads to the identification of a vascularized cancer tissue inthe at least a portion of the patient represented by the time series ofimages. In yet another example, the degree of correlation and/or natureof correlation, between the time series of images and the time series ofcardiovascular data, found in step 310 leads to a determination of theamount or type of vascularized tissue in the at least a portion of thepatient represented by the time series of images. Step 320 may beperformed separately for different spatial ROIs within the at least aportion of the patient represented by the time series of images, withoutdeparting from the scope hereof. In an embodiment, step 320 determinesseveral properties of the at least a portion of the patient representedby the time series of images.

Step 320 may be performed by processing device 210, another systemexternal to system 200, or by an operator or a physician. In oneexample, processor 230 retrieves correlation results from correlationresults storage 258 and executes instructions 260 to derive a propertyof area of interest 145 from the correlation results. In anotherexample, the property of area of interest 145 is determined externallyto system 200 based upon the correlation results determined in step 310.

In an optional step 330, a diagnostic result is derived from theproperty or properties of the at least a portion of the patient, whichare determined in step 320. Exemplary diagnostic results includepresence of benign tumor, presence of malignant tumor, absence ofmalignant tumor, and degree of vascularization of malignant tumor.

Optionally, method 300 includes a step 350 of generating at least onecorrelation-indicating image. The at least one correlation-indicatingimage is, for example, a spatial map of a parameter that indicatesdegree of correlation between the time series of images and the timeseries of cardiovascular data. Exemplary parameters are discussed belowin connection with FIGS. 5 and 6. The at least onecorrelation-indicating image may be a time series ofcorrelation-indicating images showing the time series of imagesprocessed in step 310 with an overlay that indicates the degree ofcorrelation between the time series of images and the time series ofcardiovascular data.

In an embodiment, method 300 includes a step 302 of generating the timeseries of images. For example, imaging device 110 (FIGS. 1 and 2)generates the time series of images. In one embodiment of step 302, theimaging device directly captures the time series of images of at least aportion of the patient. In another embodiment, step 302 includes steps304 and 306. In step 304, the imaging device captures a time series ofimage data that is in non-image form. In step 306, a time series ofimages is reconstructed from the time series of image data. For example,imaging device 110 captures a time series of image data that is not inimage form, such as EIT voltages. Imaging device 110 communicates thetime series of image data to processing device 210. Processor 230receives the time series of image data from interface 220 andreconstructs, according to image reconstruction instructions 268 thetime series of images from the time series of image data. Although notillustrated in FIG. 3, steps 306 may be initiated before completing step304, without departing from the scope hereof. For example, step 306 isengaged to reconstruct each single image upon completion of theassociated image data capture measurement. Thus, steps 304 and 306 maybe performed alternatingly to reconstruct one image at a time, orconcurrently at least in part.

In an embodiment, method 300 includes a step 308 of recording the timeseries of cardiovascular data. For example, cardiovascular measurementdevice 120 (FIGS. 1 and 2) records the time series of cardiovasculardata. In embodiments of method 300 based upon capture of a time seriesof image data in non-image form, step 308 is performed concurrently withstep 304. In embodiments of method 300, wherein step 302 is based upondirect capture of a time series of images, step 308 is performedconcurrently with step 302.

Optionally, the performance of step 302 is gated according by step 308such that the images, or image data, are captured at specific temporalpoints relative to the timing of heartbeat cycles. This may bebeneficial when using imaging modalities operating at rates too slow toadequately capture cardiovascular dynamics, in which case temporalaccuracy may instead be achieved through gating.

FIG. 4 illustrates one exemplary method 400 for generating a time seriesof EIT images of at least a portion of a patient. Method 400 is anembodiment of step 302 of method 300 (FIG. 3). Method 400 includes astep 410 of measuring a time series of a plurality of voltages at arespective plurality of spatially separated electrodes in contact with apatient. This time series of voltages is an embodiment of time series ofimage data 150 (FIGS. 1 and 2). In one embodiment, the voltages aremeasured in a single-frequency EIT measurement, wherein asingle-frequency alternating current or voltage is applied to one ormore spatially separated electrodes connected to the patient. In anotherembodiment, the voltages are measured in a multi-frequency EITmeasurement, wherein two or more time series single-frequency EITmeasurements, at two or more different respective frequencies, aremeasured in an interlaced fashion or simultaneously using compositesignals that include several different frequencies. Multi-frequency EITbased upon processing of composite signals including several differentfrequencies may operate at a higher sample rate than multi-frequencysystems based upon an interlaced approach. In an example of step 410,imaging device 110 (FIGS. 1 and 2) is an EIT device that measures a timeseries of a plurality of voltages at a respective plurality of spatiallyseparated electrodes in contact with patient 140 (FIGS. 1 and 2) neararea of interest 145 (FIGS. 1 and 2). The EIT device may apply a singlefrequency to the electrodes to perform a single-frequency EITmeasurement, or apply multiple different frequencies to the electrodesto perform a multi-frequency EIT measurement. Imaging device 110communicates the time series of voltages, single-frequency ormulti-frequency, to interface 220 of processing device 210 (FIG. 2).Processor 230 may store the time series of voltages to image datastorage 252 (FIG. 2). Step 410 is an embodiment of step 304 (FIG. 3).

In a step 420, a time series of EIT images is reconstructed from thetime series of voltages measured in step 410. In embodiments utilizingmulti-frequency EIT, step 420 considers each frequency separately. Inthe following, step 420 is discussed for a single frequency. In anexample of step 420, processor 230 retrieves the time series ofvoltages, measured in step 410, from image data storage 252. Processor230 utilizes image reconstruction instructions 268 to reconstruct an EITimage from each time point of the series of voltages. Thereby, processor230 reconstructs a time series of EIT images from the time series ofvoltages. Step 420 is an embodiment of step 306 (FIG. 3). Imagereconstruction in step 420 may utilize a finite element based lineardifference algorithm, a boundary element method, back projection, orother methods known in the art.

The EIT images reconstructed in step 420 provide a representation of aspatial impedance map of at least a portion of the patient as a functionof time. The spatial impedance map is represented, for example, in termsof electrical conductivity, resistance, or impedance. In certainembodiments, step 420 includes a step 430, wherein the time series ofEIT images is reconstructed as a time series of spatial distributions ofelectrical conductivity changes, as compared to a reference electricalconductivity distribution. The reference electrical conductivitydistribution may be measured by the EIT device used to perform step 410,or derived from the time series of voltages measured in step 410. In anexample, processor 230 utilizes image reconstruction instructions 268 toderive a reference electrical conductivity distribution from the timeseries of voltages measured in step 410 and further reconstruct a timeseries of spatial distributions of electrical conductivity changesrelative to the reference electrical conductivity distribution.

Although not illustrated in FIG. 4, step 420 may be initiated beforecompleting step 410, without departing from the scope hereof. Forexample, step 420 is engaged to reconstruct each single EIT image uponcompletion of the associated voltage measurement. Thus, steps 410 and420 may be performed alternatingly to reconstruct one EIT image at atime, or concurrently at least in part.

FIG. 5 illustrates one exemplary method 500 for evaluating correlationbetween a time series of images of at least a portion of a patient and atime series of cardiovascular data of the patient, wherein the timeseries of images and the time series of cardiovascular data are recordedat different series of time. Method 500 may be implemented in method 300(FIG. 3) as at least a portion of step 310. In a step 510, method 500receives a time series of images, wherein the images or underlying imagedata is captured at a first series of times. In an example of step 510,interface 220 of processing device 210 (FIG. 2) receives time series ofimage data 150 (FIGS. 1 and 2). If time series of image data 150 is notin image form, processor 230 reconstructs a time series of images fromtime series of image data 150 according to image reconstructioninstructions 268 (FIG. 2). Processor 230 stores the time series ofimages to image data storage 252 (FIG. 2). In a step 520, method 500receives a time series of cardiovascular data recorded at a secondseries of times different from the first series of times. For example,processor 230 receives time series of cardiovascular data 160 (FIGS. 1and 2) from cardiovascular measurement device 120 (FIGS. 1 and 2)through interface 220 and stores time series of cardiovascular data 160to cardiovascular data storage 254 (FIG. 2).

In a step 530, the time series of images and/or the time series ofcardiovascular data are resampled to synchronize the first series oftimes with the second series of times. Herein, the first series of timesand the second series of times are not necessarily constants. Rather,the first series of times and the second series of times refer to theseries of times associated with the time series of images and the timeseries of cardiovascular data, respectively, at any point duringprocessing. Hence, the first series of times and/or the second series oftimes are modified in step 530. In one embodiment, one of the timeseries of images and the time series of cardiovascular data is resampledto match the time series associated with the other one of the timeseries of images and the time series of cardiovascular data. Forexample, the lower-rate time series, such as the time series of images,is resampled to match the higher-rate time series, such as the timeseries of cardiovascular data. Step 530 may include a step 532 ofinterpolating between measured time points of at least one of timeseries of images and time series of cardiovascular data. In an exampleof step 530, processor 230 retrieves the time series of images fromimage data storage 252 and resamples the time series of images accordingto resampling instructions 263 (FIG. 2) to synchronize the time seriesof images with time series of cardiovascular data 160.

A step 540 evaluates correlation between the mutually synchronized timeseries of images and time series of cardiovascular data received fromstep 530. Since step 540 considers mutually synchronized time series ofimages and time series of cardiovascular data, the correlation betweenthe time series of images and the time series of cardiovascular data maybe evaluated in a relatively simplistic fashion, such as through visualcomparison. In an alternate example, the mutually synchronized timeseries of images and time series of cardiovascular data are processedmathematically to evaluate the degree and/or nature of correlationtherebetween. For example, processor 230 evaluates correlation betweenthe mutually synchronized time series of images and time series ofcardiovascular data 160, according to correlation instructions 262 (FIG.2), and stores the correlation results to correlation results storage258 (FIG. 2) or outputs the correlation results via interface 220.

In an embodiment, step 540 includes one or both of steps 542 and 544. Instep 544, the spectral correlation between the mutually synchronizedtime series of images and time series of cardiovascular data isevaluated. For example, processor 230 evaluates the spectral correlationbetween the mutually synchronized time series of images and time seriesof cardiovascular data according to spectral correlation instructions265 (FIG. 2). Processor 230 stores the spectral correlation results tocorrelation results storage 258 or outputs the spectral correlationresults via interface 220. In step 542, the temporal correlation betweenthe mutually synchronized time series of images and time series ofcardiovascular data is evaluated. For example, processor 230 evaluatesthe temporal correlation between the mutually synchronized time seriesof images and time series of cardiovascular data according to temporalcorrelation instructions 266 (FIG. 2). Processor 230 stores the temporalcorrelation results to correlation results storage 258 or outputs thetemporal correlation results via interface 220.

Step 540 may further include a step 546 which is identical to step 316of method 300 (FIG. 3).

Without departing from the scope hereof, steps 510 and 530 may beperformed on a time series of image data in non-image form. In thiscase, reconstruction of the time series of image data to generate a timeseries of images may be performed after step 530.

FIG. 6 illustrates one exemplary method 600 for analyzing correlationbetween a time series of images of at least a portion of a patient andheartbeat cycles identified from a time series of cardiovascular data ofthe patient. Method 600 is an embodiment of step 316 of method 300 (FIG.3). In a step 610, at least one heartbeat cycle is identified in thetime series of cardiovascular data through detection of at least onecardiovascular signature in the time series of cardiovascular data. Forexample, processor 230 (FIG. 2) processes time series of cardiovasculardata 160 (FIGS. 1 and 2), according to signature identificationinstructions 264 (FIG. 2), to detect at least one cardiovascularsignature such as a peak associated with each heartbeat cycle. Basedupon the cardiovascular signature and according to signatureidentification instructions 264, processor 230 identifies at least oneheartbeat cycle in time series of cardiovascular data 160.

In an optional step 620, a time series of position sensitive signals,each representing the same spatial ROI, is extracted from the timeseries of images. In an embodiment, each position sensitive signal inthe time series of position sensitive signals is an average of thecorresponding image within the spatial ROI. For example, processor 230retrieves the time series of images from image data storage 252 (FIG. 2)and retrieves a spatial ROI definition from spatial ROI definitions 256(FIG. 2). Processor 230 then extracts a time series of positionsensitive signals from the time series of images according to thespatial ROI definition and correlation instructions 262 (FIG. 2).Optional step 620 may be preceded by a step 601 of defining the spatialROI. In one embodiment, the spatial ROI is defined by an operator or byprocessing device 210 (FIG. 2) based upon images provided by the imagingdevice used to generate the time series of images or image data. Inanother embodiment, the spatial ROI is defined by an operator or byprocessing device 210 based upon images provided by another imagingdevice utilizing a different imaging modality. In yet anotherembodiment, the spatial ROI is predefined.

Without departing from the scope hereof, steps 620, 630, and 640, andoptionally step 601, may be performed for several different spatialROIs.

In a step 630, the time series of images (or position sensitive signalsin embodiments of method 600 including optional step 620) is referencedto the timing of heartbeat cycles as identified in step 610. Forexample, processor 230 references the time series of images (or positionsensitive signals) to timing of heartbeat cycles according tocorrelation instructions 262.

In an embodiment, and if the rate of heartbeats identified in step 610is not constant, step 630 includes a step 632, wherein the time seriesof cardiovascular data is resampled to emulate a constant heartbeatrate. The time series of images (or position sensitive signals) isresampled accordingly such that the temporal relationship between thetime series of cardiovascular data and the time series of images (orposition sensitive signals) is not distorted by the performance of step632. In an example of step 632, processor 230 resamples the time seriesof cardiovascular data and the time series of images (or positionsensitive signals) according to resampling instructions 263 (FIG. 2).

In a step 640, the time series of images (or position sensitive signals)is analyzed as a function of timing within a heartbeat cycle. Step 640includes step 646 and/or step 642. Step 642 determines one or moreparameters indicative of spectral and/or temporal correlation betweenthe time series of images (or position sensitive signals) and heartbeatcycles. Exemplary parameters include spectral correlation coefficient,temporal correlation coefficient (optionally corrected for a phase shiftbetween the time series of images and the time series of cardiovasculardata, for example based upon a premeasured value of the phase shift),maximum temporal correlation coefficient as a function of phase shiftbetween the time series of images (or position sensitive signals) andheartbeat cycles, phase at which the maximum temporal correlationcoefficient is attained, and spectral power ratios of the time series ofimages (or position sensitive signals). The spectral power ratiosindicate the degree of spectral correlation between the time series ofimages (or position sensitive signals) and heartbeat cycles. In anexample of step 642, processor 230 utilizes spectral correlationinstructions 265 (FIG. 2) or temporal correlation instructions 266 (FIG.2) to determine one or more parameters indicative of spectral and/ortemporal correlation. Processor 230 may store such parameters toparameter storage 259 (FIG. 2).

Optionally, step 642 is succeeded by a step 644, in which two or moreparameters determined in step 642 are combined to further evaluate thedegree and/or nature of correlation between the time series of images(or position sensitive signals) and heartbeat cycles. Step 644 mayevaluate a combination of parameters against correlation criteria. In anexample of step 644, processor 230 retrieves two or more parameters fromparameter storage 259 and combines these parameters according tocorrelation instructions 262.

In step 646, spectral and/or temporal distributions of time series ofimages (or position sensitive signals) are compared to those of the timeseries of cardiovascular data reflecting a constant heartbeat rate. Forexample, the time series of images (or position sensitive signals) andthe time series of cardiovascular data received from step 630 areoutputted to an operator or physician via interface 220 (FIG. 2). Thephysician or operator compares the spectral and/or temporaldistributions of the time series of images (or position sensitivesignals) and the time series of cardiovascular data reflecting constantheartbeat rate.

In an embodiment, method 600 takes as input mutually synchronized timeseries of cardiovascular data and time series of images, such as thosegenerated by step 530 of method 500 (FIG. 5).

FIG. 7 illustrates one exemplary method 700 for evaluating temporalcorrelation between a time series of images of at least a portion of apatient and a time series of cardiovascular data of the patient. Method700 may be implemented in method 300 (FIG. 3) as at least a portion ofstep 314, in method 500 (FIG. 5) as at least a portion of step 542, orin method 600 (FIG. 6) as at least a portion of step 642.

In a step 710, the temporal correlation coefficient between a timeseries of position sensitive signals and the time series ofcardiovascular data is calculated as a function of phase shift betweenthe time series of position sensitive signals and the time series ofcardiovascular data. The time series of position sensitive signals maybe derived from the time series of images as discussed in connectionwith step 620 (FIG. 6). The time series of images and the time series ofcardiovascular data may be mutually synchronized as discussed inconnection with step 530 (FIG. 5). Additionally, the time series ofimages and the time series of cardiovascular data may reflect a constantheartbeat rate, optionally after resampling as discussed in connectionwith step 632 (FIG. 6).

Since the time series of images and the time series of cardiovasculardata may be obtained in two different locations of the patient, a phaseshift may exist between the time series of images and the time series ofcardiovascular data. Furthermore, the imaging device and cardiovascularmeasurement device used to generated the time series of images and thetime series of cardiovascular data, respectively, may introduceadditional phase shifts of unknown magnitude. If such phase shifts arenot taken into account, for example by premeasuring the phase shift andcorrecting therefor, the calculated temporal correlation coefficient maybe lower than the actual temporal correlation coefficient obtained whencorrecting for phase shift between the time series of images and thetime series of cardiovascular data. Step 710 overcomes this issue byvarying the phase shift between the time series of images and the timeseries of cardiovascular data and calculating the temporal correlationcoefficient therebetween for a range of phase shifts, such as severaldifferent phase shifts substantially spanning the full range of phaseshifts.

In an example of step 710, processor 230 (FIG. 2) retrieves the timeseries of position sensitive signals from image data storage 252 (FIG.2) and the time series of cardiovascular data from cardiovascular datastorage 254 (FIG. 2). Processor 230 utilizes temporal correlationinstructions 266 to calculate the temporal phase shift between the timeseries of position sensitive signals and the time series ofcardiovascular data as a function of phase shift therebetween. Processor230 stores this temporal correlation coefficient as a function of phaseshift to correlation results storage 258 (FIG. 2).

In a step 720, method 700 determines at least one of (a) the maximumtemporal correlation coefficient as a function of phase shift and (b)the phase shift at which the maximum temporal correlation coefficient.For example, processor 230 retrieves the temporal correlationcoefficient as a function of phase shift from correlation resultsstorage 258 and determines, according to temporal correlationinstructions 266, the maximum temporal correlation coefficient and/orthe phase at which the maximum temporal correlation coefficient isattained.

FIG. 8 illustrates a method 800 for evaluating spectral correlationbetween a time series of images of at least a portion of a patient and atime series of cardiovascular data of the patient. Method 800 may beimplemented in method 600 (FIG. 6) as at least a portion of step 642. Ina step 810, the spectral distribution of a time series of positionsensitive signals is evaluated. Optionally, step 810 includesdetermining the spectral distribution of the time series of positionsensitive signals, for example using a fast Fourier transform. The timeseries of position sensitive signals is assumed to reflect a constantheartbeat rate and is, for example, generated in step 632 (FIG. 6).Since the time series of position sensitive signals correspond to aconstant heartbeat rate, the spectral distribution of the time series ofposition sensitive signals indicates the degree and/or nature ofspectral correlation between the time series of position sensitivesignals and heartbeat cycles. In an embodiment, step 810 includes a step812 of calculating one or more power ratios of the spectral distributionof the time series of position sensitive signals. A spectral power ratiois the ratio of power in one spectral range to the power in anotherspectral range. The two spectral ranges may be non-overlapping orpartially overlapping.

In an example of steps 810 and 812, processor 230 (FIG. 2) utilizesspectral correlation instructions 265 (FIG. 2) to calculate the powerspectrum of the time series of position sensitive signals, reflecting aconstant heartbeat rate, and further calculate therefrom one or morespectral power ratios.

FIG. 9 illustrates one exemplary system 900 for cardiovascular dynamicscorrelated imaging of area of interest 145 of patient 140 (FIGS. 1 and2), which utilizes two different imaging devices. System 900 is similarto system 200 (FIG. 2). As compared to system 200, system 900 furtherincludes a second imaging device 910 that generates images differentfrom those of imaging device 110 (FIGS. 1 and 2). Additionally,processing device 210 (FIG. 2) is replaced with a processing device 912.Processing device 912 is similar to processing device 210 except formemory 240 (FIG. 2) being replaced with a memory 940. In turn, memory940 is similar to memory 240 except for instructions 260 (FIG. 0.2)being replaced by instructions 960 which, as compared to instructions260 may further include image overlay instructions 962 and or spatialROI selection instructions 964.

Second imaging device 910 generates one or more second-type images 950of a second area of interest 945 of patient 140. Second area of interest945 has at least some overlap with area of interest 145 (FIGS. 1 and 2).Second-type images 950 are of type different from the images associatedwith time series of image data 150 (FIGS. 1 and 2). Second imagingdevice 910 communicates second-type image(s) 950 to interface 220 (FIG.2) of processing device 912. In one embodiment, second imaging device910 is of type different from imaging device 110. In another embodiment,second imaging device 910 is of same type as imaging device 110, bututilized to generate a different type of images. For example, secondimaging device 910 is an MRI device generating anatomic MRI images,while imaging device 110 is an MRI device generating cardiovascularemphasized images.

In one embodiment, instructions 960 of processing device 912 includesimage overlay instructions 962, that upon execution by processor 230(FIG. 2) overlays one image or time series of images on another image ortime series of images. For example, processor 230 may execute imageoverlay instructions 962 to overlay one or more correlation-indicatingimages, generated from time series of image data 150 and time series ofcardiovascular data 160 (FIGS. 1 and 2) by performing method 300 (FIG.3) with step 350, on second-type image(s) 950. Processor 230 may storesuch an overlaid image to image data storage 252 and/or communicate suchan overlaid image to an external system or user via interface 220.

In another embodiment, instructions 960 includes spatial ROI selectioninstructions 964 that upon execution by processor 230 analyzessecond-type image(s) 950 to determine one or more spatial ROIs to beconsidered by processing device 912 when evaluating correlation betweentime series of image data 150 and time series of cardiovascular data160, for example as discussed in connection with method 600 (FIG. 6).Processor 230 may store such spatial ROIs to spatial ROI definitions 256(FIG. 2), from where processor 230 may retrieve one or more spatial ROIswhen performing method 600.

Second imaging device 910 is, for example, an EIT device, an MRI device,a CT device, and ultrasonograph, a video endoscope, or a fluoroscope. Incertain embodiments of system 900, imaging device 110 is an EIT deviceand second imaging device 910 is a non-EIT device such as an MRI device,a CT device, and ultrasonograph, a video endoscope, or a fluoroscope.This embodiment benefits from correlation of time series ofcardiovascular data 160 with time series of image data 150 generated byan imaging device that is very sensitive to cardiovascular dynamics, asdiscussed in connection with FIG. 1, which leads to high-qualitycardiovascular-dynamics correlation information. System 900 is capableof combining such cardiovascular-dynamics correlation information withsecond-type image(s) 950 which may provide information about area ofinterest 145 not achievable using imaging device 110.

FIG. 10 illustrates one exemplary method 1000 for overlaying acorrelation-indicating image, indicating correlation between a timeseries of images of at least a portion of a patient and a time series ofcardiovascular data of the patient, on a time series of images. Method1000 may be performed by system 900 of FIG. 9.

Method 1000 is similar to method 300 of FIG. 3 and includes steps 1010and 1050, and optionally one or more of steps 1002, 1004, 1020, and1030, wherein method 1000 performs respective steps 310 and 350 andoptional steps 302, 308, 320, and 330 as discussed in connection withFIG. 3. Additionally, method 1000 includes a step 1060 wherein thecorrelation-indicating image(s) generated in step 1050 are overlaid on atime series of images.

In one embodiment, step 1060 includes a step 1062 of overlaying thecorrelation-indicating image on the time series of images processed instep 1010. For example, processor 230 of system 900 (FIG. 9) executesimage overlay instructions 962 (FIG. 9) to overlay thecorrelation-indicating image on time series of image data 150 in imageformat (FIGS. 1 and 9).

In another embodiment, step 1060 includes a step 1064 of overlaying thecorrelation-indicating image on a time series of second-type imagesgenerated by an imaging device of type different from the one used togenerated the time series of images processed in step 1010. For example,processor 230 of system 900 (FIG. 9) executes image overlay instructions962 (FIG. 9) to overlay the correlation-indicating image on a timeseries of second-type images 950 (FIG. 9). In this embodiment, method1000 may further include a step 1006 of generating the time series ofsecond-type images. The time series of second-type images, or underlyingsecond-type image data, may be captured concurrently with the timeseries of image data used to generate the time series of imagesprocessed in step 1010. For example, second imaging device 910 (FIG. 9)captures a time series of second-type images 950 (FIG. 9) concurrentlywith imaging device 110 (FIGS. 1 and 9) capturing time series of imagedata 150 (FIGS. 1 and 9).

FIG. 11 illustrates one exemplary method 1100 for overlaying at least aportion of a time series of first-type images of a first area ofinterest of a patient on a time series of second-type images of a secondarea of interest of the patient, wherein the first and second areas ofinterest at least partially overlap. The second-type images are of typedifferent from the first-type images. Method 1100 may be performed bysystem 900 (FIG. 9). Method 1100 is similar to method 1000 (FIG. 10)except that step 1060 is replaced with a step 1160.

In step 1160, at least a spatial portion of the time series of imagesprocessed in step 1010, which meets specified requirements to the degreeand/or nature of correlation with the time series of cardiovasculardata, is overlaid on a time series of second-type images. For example,processor 230 (FIGS. 2 and 9) executes image overlay instructions 962(FIG. 9) to overlay a spatial portion of time series of image data 150(FIGS. 1 and 9), or a time series of images generated therefrom, whichmeets requirements to correlation with time series ofcorrelation-indicating image on a time series of second-type images 950(FIG. 9).

EXAMPLE I: Real-time Electrical Impedance Variations in Phantoms and inWomen with and without Breast Cancer

In this Example, an embodiment of system 100 (FIG. 1) performscardiovascular-dynamics correlated imaging of (a) pulsating phantoms and(b) breasts of female patients according to an embodiment of method 300(FIG. 3). In the embodiment of system 100 utilized in this Example,imaging device 110 is an EIT device and cardiovascular measurementdevice 120 is a pulse-oximeter.

Imaging Breast Tumor Hemodynamics

Neovasculature occurs primarily at a tumor's periphery while its centertends to undergo necrosis. The physiological phenomena occurring withinthe microvasculature surrounding the tumor is complex, and studies haveshown this network of vessels to be chaotically arranged, permeablecapillaries. Benign lesions are vascularized as well, but they are morewell-defined and lack the chaotic structures of many malignant tumors.

As blood flows through the vasculature it experiences a periodicpulsatillity synchronized to the beating of the heart. The presence ofthe excessive chaotically arranged vasculature around a malignant tumormay present a different dynamic electrical impedance signature than thatobtained from normal and benign tissues within the breast. A high-speed,precise EIT system may be capable of imaging this periodic blood flow byrecording measurements at various phases within the cardiovascularcycle. Further, collecting data at specific phases within the cardiaccycle, but over multiple cycles, will provide a multiplicity of datathat can be averaged to increase noise suppression and improve imagecontrast. This would provide a new contrast mechanism that is differentfrom that available through static and multi-frequency EIT which mayprove more successful at differentiating breast tissues based on thedynamic characteristics of their blood flow instead.

EIT Imaging and Image Synchronization

Dartmouth's third generation breast EIT system, an embodiment of imagingdevice 110 (FIG. 1), is capable of collecting high frame rate data, >40frames per second (fps), as described previously in R. J. Halter, A.Hartov, K. D. Paulsen, “A broadband high frequency electrical impedancetomography system for breast imaging,” IEEE Transactions on BiomedicalEngineering, vol. 55, pp. 650-59, 2008 and in R. J. Halter, A. Hartov,K. D. Paulsen, “Video Rate Electrical Impedance Tomography of VascularChanges: Preclinical Development,” Physiological Measurement, vol. 29,no. 3, pp. 349-364, 2008, both of which are incorporated by referenceherein in their entireties. Briefly, this EIT system is a wide-bandwidth(10 kHz-10 MHz), 64 electrode, voltage-driven system specificallydesigned for use in breast imaging. In high-speed acquisition mode itcollects bursts of 40 frames of data at a user specified frame rate (upto 180 fps); following acquisition the data is off-loaded from thesystem electronics to an interface computer for post-processing andimage reconstruction. The computer is an embodiment of processing device130 (FIG. 1).

The system has been upgraded with a cardiovascular monitoring unit (CMU)to permit external triggering of data collection. Specifically, aUSB-based analog signal capture device (PMD-1608FS, MeasurementComputing, Massachusetts, USA) interfaced to the EIT system computer (anembodiment of processing device 130 (FIG. 1)) is used to record anexternal biophysical signal. The PMD-1608FS is coupled with apulse-oximetry sensor to form an embodiment of cardiovascularmeasurement device 120 (FIG. 1). The PMD-1608FS has eight single-endedanalog inputs that are captured and converted to the digital domain with16-bit ADCs. The EIT system software continuously scans a single analoginput channel at a rate of 62.5 Hz and a software-based thresholddetection scheme is used to trigger EIT data acquisition. Thresholds aremanually selected based on the characteristics of the input signal. Thisprovides the ability to trigger EIT data acquisition synchronously withthe QRS complex of an ECG waveform or the peak in the oxygen saturationsignal sampled from a pulse-oximeter. The cardiovascular signal isrecorded simultaneously while EIT data acquisition occurs. Followingacquisition of a 40 frame burst of data, additional bursts of frames canbe recorded. When an arbitrary number of bursts are collected, dataacquisition is halted and the data is transferred for offline imagereconstruction.

For the phantom experiments and patient series reported here, the systemwas configured to image a single plane, consisting of a circular ring of16 electrodes. The 15 optimal trigonometric voltage patterns for a 16electrode system, as defined in D. Isaacson, “Distinguishability ofconductivities by electric current computed tomography,” IEEE Trans.Med. Imaging, vol. 5, pp. 91-95, 1986 which is incorporated by referenceherein in its entirety, were used as the driving patterns. Eachacquisition frame consisted of 240 voltages measurements (15 patterns x16 electrodes) which were arranged in the column vector V_(i). Eachforty-frame burst of recorded voltages defined a non-square matrix,V_(n)=[V₁ V₂ V₃ . . . V₄₀], where n represents the burst number whenmultiple bursts of image frames are collected. The correspondingbiophysical signal sampled at 62.5 Hz defined a column vector y_(n)=[y₁,y₂, y₃, . . . y_(Nc,samples)] for each 40-frame burst, where n=1, 2, 3,. . . N_(burst), and N_(c,samples), the length of y_(i), is based on theduration of the 40-frame EIT burst collection window. This length is afunction of the EIT frame rate and for the data presented here is fixedat 17.3 fps. The EIT data collection method described in this Example isan embodiment of step 410 (FIG. 4). The series of V_(n) is an embodimentof time series of image data 150 (FIG. 1). The series of y_(n) is anembodiment of time series of cardiovascular data 160 (FIG. 1).

Image Reconstruction

Image reconstruction is performed according to step 420 with steps 430and 432 (FIG. 4). This Example used a finite element (FEM) based lineardifference algorithm to estimate the changing conductivity distributionbetween frames. A 2D circular mesh with 640 elements, 353 nodes andscalable diameter was generated to model the experimental geometry. Thechange in conductivity, Δσ_(i), at each of the mesh nodes is calculatedfrom Δσ_(i)=(J^(T)J+λL^(T)L)⁻¹ J^(T) {V^(ref)−V_(i)}, i∈1, 2, 3, . . . ,40, (Eq. 1) where J is a Jacobian matrix representing the sensitivity ofchanges in boundary voltages to changes in conductivity, L is aLaplacian regularization matrix, and λ is a regularization parameterused to stabilize the inversion. V^(ref) is a set of boundary voltagesdesignated as a reference conductivity distribution, while V_(i) is theset of boundary voltages collected during each frame i. J is computedfrom the reference boundary voltages via the adjoint method disclosed inN. Polydorides and W. R. B. Lionheart, “A Matlab toolkit forthreedimensional electrical impedance tomography: a contribution to theelectrical impedance and diffuse optical reconstruction softwareproject,” Meas. Sci. Technol., vol. 13, pp. 1871-1883, 2002 which isincorporated by reference herein in its entirety, and is fixed for eachΔσ_(i) calculation. The vector Δσ_(i) represents the 353 nodal change inconductivity values at each frame i and is computed using the change inmeasured boundary voltages with respect to the reference. V_(ref) isdefined as the mean of all voltages recorded from a single burst ofdata,

$V^{ref} = {{\overset{\_}{V}}_{n} = {\frac{1}{N}{\sum\limits_{1}^{40}{V_{i}.}}}}$Taking the mean across all frames provides a less noisy reference fromwhich to calculate conductivity changes. Δσ_(i) is calculated using Eq.1 for each of the 40 frames recorded from a single acquisition burst.Empirical testing demonstrated that a λ of 0.001 provided a sufficientlevel of regularization to ensure stable inversion. Within each burst,Δσ_(n)=[Δσ₁ Δσ₂ Δσ₃ . . . Δσ₄₀] represents the spatiotemporal sequenceof changing conductivity estimates.

Correlation Evaluation

FIG. 12 illustrates a method 1200 used in this Example for evaluatingcorrelation between the time series of EIT images and the time series ofpulse-oximeter measurements. Method 1200 is an embodiment of method 300(FIG. 3).

Following data acquisition and Δσ image reconstruction, the temporaldata (Δσ images, Δσ_(n), and biophysical signal, y_(n)) were analyzed inorder to assess (a) the beat-to-beat correspondence between thecardiovascular and Δσ signatures and (b) the inter-beat statistics ofthese signals. The cardiovascular signal being sampled (either the ECGor pulse-ox waveform) provided a surrogate measure of blood flow throughthe breast and was the means of comparison to which the temporalconductivity changes were evaluated. Note that this signal provided onlya relative reference for comparison since it is dependent on thelocation of the pulse-ox (i.e. finger-based vs. carotid sampling).Sampling 40 frames/burst at 17.3 fps provided a 2.3 second samplingwindow. Resting heart rates typically ranged from 50-90 bpm (0.83-1.5beats per second (bps)) resulting in 1.9 to 3.45 heart beats beingsampled per burst during this 2.3 second window. Relatively largeconductivity changes were present during phantom imaging and thesignatures acquired from a single acquisition burst (40 frames) providedsufficient detail for identifying the temporal and spectralcharacteristics of the spatio-temporal conductivity variations induced.However, for dynamic breast imaging, the conductivity changes are muchsmaller and require additional processing over multiple bursts tocharacterize the signals. Specifically, a single heart beat wasextracted from each 40-frame burst to provide multiple cardiac eventsfor processing.

In a step 1210, method 1200 receives N_(burst) time series of imagesΔσ_(n) (the temporal change in conductivity sampled at an interval ofT_(σ)=57.8 ms) and N_(burst) time series of cardiovascular data y_(n)(the cardiovascular signal being monitored at a sampling interval ofT_(c)=16 ms).

In a step 1220, method 1200 resamples the N_(burst) time series ofimages Δσ_(n) to match the series of times associated with the timeseries of cardiovascular data y_(n). Step 1220 is an embodiment of step530 (FIG. 5). Because T_(c)≠T_(σ). Δσ_(n) was resampled using cubicspline interpolation to match the sampling rate of y_(n). Thisresampling was performed over each of the image nodes and ensured thatthe resampled signal, {circumflex over (Δ)}σ_(n), and y_(n) were of thesame length, with each sample occurring at equivalent instances in time.Each 40-frame burst was resampled in a similar fashion.

In a step 1230, method 1200 identifies and extracts a single heartbeatcycle in each time series of cardiovascular data y_(n). Step 1230 is anembodiment of step 610 (FIG. 6). Step 1230 and subsequent steps 1240,1250, 1260 and 1270 are collectively an embodiment of method 600 (FIG.6). A single cardiac cycle was extracted from each burst. This wasaccomplished by determining successive peaks within the cardiovascularsignal, y_(n), and extracting the corresponding temporal points fromwithin the {circumflex over (Δ)}σ_(n) sequence. Prior to peak detection,the cardiovascular signal at each time instant i was demeaned andnormalized. Identifying the peak depended on the signal sampled and forthe case of pulse-oximetry signatures, a derivative-based algorithm wasemployed as a simple, accurate and efficient mode of peak detection. Tothis end, the first-order difference, Δ, is calculated across the entiretemporal sequence and normalized: Δ=y_(i+1)−y_(i) (i∈1, 2, 3, . . .N_(c,samples)−1) and {circumflex over (Δ)}=Δ/max(Δ). A peak was definedas the time at which the maximum {circumflex over (Δ)} occurred within apredefined window beginning at the time point at which {circumflex over(Δ)} exceeded a specified threshold. The threshold, τ, was empiricallydetermined by trial and error. The first peak, Γ₁, was defined as themaximum {circumflex over (Δ)} found within the window, ψ, following thefirst instance at which τ was exceeded, i_(τ): Γ=max({circumflex over(Δ)}_(i) _(τ) , {circumflex over (Δ)}_(i) _(τ+1) , {circumflex over(Δ)}_(i) _(τ+2) , . . . , {circumflex over (Δ)}_(i) _(τ+ψ) ). For thepulse-oximetry signals acquired here, a τ of 0.35 and ψ of 20 provedrobust in detecting the first oxygen saturation peak in each of theacquired bursts. The second peak, Γ₂, was similarly found by providingthe detection algorithm the cardiovascular signature ranging from theend of the first peak window, i_(τ)+ψ, to the sequence end.

In step 1240, a single cardiovascular period and the corresponding{circumflex over (Δ)}_(σ) sequence ranging from Γ₁ to Γ₂ were extractedfrom the original sequences {tilde over (y)}=ŷ(Γ₁,Γ₁+1,Γ₁+2, . . . Γ₂)and {circumflex over (Δ)}σ={circumflex over (Δ)}σ(Γ₁,Γ₁+1,Γ1+2, . . .Γ₂).

In step 1250, method 1200 resamples, for each single heartbeat cycle,the time series of images {circumflex over (Δ)}σ and the time series ofcardiovascular data {tilde over (y)} to emulate a constant heartbeatrate. Heart-rate variability is a well-established phenomenon whichmanifests itself in this Example as the temporal length of {tilde over(y)} and {circumflex over (Δ)}σ for each burst being variable. In orderto account for this variability, the extracted single-beat sequenceswere resampled using cubic spline interpolation with a fixed number ofsamples (=40) to occur over a temporal duration of 1 second, effectivelyenforcing a 1 bps heart rate. By enforcing a fixed number of samplesoccurring over the 1 second interval, the sequences extracted from eachburst were easily compared.

In step 1260, individual heartbeat cycles {tilde over (y)} and{circumflex over (Δ)}σ are concatenated to form a time series of imagesΔσ(t) and a time series of cardiovascular data y(t) that span multipleheartbeat cycles.

In step 1270, method 1200 analyzes the concatenated data generated instep 1260 to calculate parameters indicative of correlation between thetime series of images Δσ(t), for one or more selected spatial ROIs, andthe time series of cardiovascular data y(t). Image analysis consisted ofextracting the mean Δσ within a specified spatial ROI from each frame,according to output of step 601 (FIG. 6). This extraction provided atemporal sequence of Δσ_(ROI)(t) corresponding to a specific regioninside the imaging domain. The temporal signature of each sequence wasfiltered using an 81-tap Hamming window with a cutoff frequency of 8.65Hz (½ of the 17.3 Hz sampling frequency) and padded with zeroes prior totaking the 512-point Fast Fourier Transform (FFT). The power spectra,ΔΣ(f) and Y(f), were estimated as the square of the individual frequencycomponents extracted from the FFT (i.e. ΔΣ(f)=|FFT (Δσ(t))|²). Inpatients several spectral and temporal measures were used toparameterize the waveforms. Specific parameters are discussed in thefollowing:

Spectral Correlation Coefficient, r_(s): Correlation coefficient betweenthe oxygen-saturation spectra, Y(f) and the ΔΣ(f) spectra. Thecorrelation was obtained for frequencies ranging from 1 Hz to 8.65 Hz.DC to 1 Hz signals were not included because the shortest frequenciesable to be gauged were 1 Hz due to the resampling procedure.

Maximum Temporal Correlation Coefficient, r_(t,max): Maximum correlationcoefficient occurring between the oxygen-saturation signal and aphase-shifted Au signal (calculated according to an embodiment of method700 of FIG. 7). The correlation coefficient was computed at each degreeof phase shift and r_(t,max) denotes the maximum correlation coefficientobtained through the entire phase-shifting procedure.

Phase Shift φ(r_(t,max)) at r_(t,max): The phase shift leading to themaximum correlation coefficient (r_(t,max)) between the oxygensaturation signal and Δσ signal (calculated according to an embodimentof method 700 of FIG. 7). This represents the phase shift required toproduce the maximum correlation coefficient.

Spectral Power Ratio, P_(x:y): The ratio of total spectral power withinone frequency band in reference to a second frequency band as definedby: P_(x:y)=∫_(f) ₁ ^(f) ² ΔΣ(f)df/∫_(f) ₃ ^(f) ⁴ ΔΣ(f)df. Threespectral power ratio's for each ΔΣ spectrum were computed: P_(low:full)(f₁=1 Hz, f₂=4.325 Hz, f₃=1 Hz, f₄=8.65 Hz), P_(high:full) (f₁=4.325 Hz,f₂=8.65 Hz, f₃=1 Hz, f₄=8.65 Hz), and P_(low:high) (f₁=1 Hz, f₂=4.325Hz, f₃=4.325 Hz, f₄=8.65 Hz).

Phantom Imaging—Experimental Configuration

FIG. 13 illustrates the system 1300 used for the cardiovascular-dynamicscorrelated phantom imaging. A series of phantom experiments wereconducted in the same way that patient data was collected in order toevaluate system performance. An 8 cm circular tank 1310 fitted with 1 cmstainless steel electrodes 1314 was positioned within the EIT system1320. A saline solution 1312 (σ=0.113 S/m) was added to tank 1310 to aheight of 2 cm just covering the bottom layer of electrodes 1314. Apulsating latex balloon 1330 positioned within saline solution 1312 wasused to simulate a dynamically varying low conductivity volume. Atwo-port Y-type connector 1332 interfaced to balloon 1330 allowed fluidto flow in and out of the membrane. Flexible tubing 1334 extending fromone of the ports was interfaced through a programmable COBE heart-lungprecision blood pump 1340 (COBE Lakewood, Colo.) to a second saline bath1352 (σ=0.014 S/m). This solution was pumped through balloon 1330, and aflexible tube 1335 connected to the second port acted to drain fluidfrom balloon 1330. An Agilent 33120A arbitrary waveform generator 1350(Agilent Technologies, Santa Clara, Calif.) was used to generate bothsine and square waves of particular amplitudes and frequencies to drivepump 1340. The back V_(emf) signal of the pump was sensed bycardiovascular monitoring unit 1360 (PMD-1608FS discussed above) andused to trigger EIT data acquisition by EIT system 1320 through controlPC 1370.

Eight different pumping schemes were imaged. Sine wave excitations at 1Hz, 2 Hz, and 4 Hz were used, followed by square wave excitations at 1Hz, 2 Hz, and 4 Hz. The drive amplitude for each of these waveforms wasa constant 1 V_(pp), which resulted in balloon 1330 diameter changes of1-3 mm per cycle with the maximum balloon 1330 diameter extending toapproximately 2 cm. Ten 40-frame bursts were collected at 17.3 framesper second for each driving configuration at 127 kHz.

Phantom Imaging—experimental Results

FIG. 14A-14C display exemplary temporal sequences 1410, 1420, and 1430of Δσ images occurring over a single 40-frame burst for sine waveexcitation at 1 Hz, 2 Hz, and 4 Hz, respectively. Each of temporalsequences 1410, 1420, and 1430 is displayed such that the first rowshould be read from left to right, then the second row should be readfrom left to right, etc. The greyscale map used in FIGS. 14A-14Crepresents Δσ and ranges from −8 mS/m to 8 mS/m.

The pulsing balloon 1330 was well localized in each burst of frames forall excitation drive configurations. Regions of low and high Δσ,corresponding to inflation and deflation of balloon 1330, have adecreased temporal period as the excitation frequency increases. A 2 cmdiameter circular ROI was defined around the node having maximum changein conductivity over the course of the burst (x=2.2 cm, y=0.2 cm) andthe mean Δσ within the ROI from each frame was extracted.

FIGS. 15A and 15B show temporal sequences and power spectra for each ofthe drive frequencies with sine wave excitation and square waveexcitation, respectively. Plots 1512, 1522, and 1532 show temporalsequences at 1 Hz, 2 Hz, and 4 Hz drive frequency, respectively. Plots1514, 1524, and 1534 show power spectra at 1 Hz, 2 Hz, and 4 Hz drivefrequency, respectively. Likewise, plots 1542, 1552, and 1562 showtemporal sequences at 1 Hz, 2 Hz, and 4 Hz drive frequency,respectively, and plots 1544, 1554, and 1564 show power spectra at 1 Hz,2 Hz, and 4 Hz drive frequency, respectively. Solid line denotes Δσ anddashed line denotes V_(emf) sensed by the EIT system for triggering.Note that in square wave variation, harmonics in Δσ occur at 1 Hz, whichare absent in the higher frequencies because of the low pass filtereffect of the long fluid paths within the system.

The temporal sequences and power spectrum from each of the driveconfigurations demonstrates that the temporal changes in conductivityand its spectral signature correlated well with that of the recordedpump drive voltage, V_(emf). The phase shift noted between the temporalΔσ and V_(emf) traces is due to the phase introduced by the hydrodynamiccoupling between the pump and fluid network. During sine waveexcitation, the principal spectral component for each driveconfiguration coincided with the programmed drive frequency at 1 Hz, 2Hz, and 4 Hz. The additional peak in the 2 Hz and 4 Hz configurations at˜1 Hz is due to rigid balloon 1330 translation and saline solutiondisplacement as the tubing moved during the pumping procedure. Duringsquare wave excitation, pump 1340 was unable to generate a square waveat 4 Hz due to inertial damping within the motor. At 1 Hz and 2 Hz,however, pump 1340 was able to specify a square wave rotation (see FIG.15B). As expected the hydrodynamic network interfacing pump 1340 andreservoir to balloon 1330 acted as a low pass filter. As a result, theΔσ images acquired during square wave excitation do not clearly displaythe sharper edges (high frequencies) present in the recorded V_(emf) ofpump 1340. However, within the spectral domain, the Δσ signatures at 1Hz show both the principal component at 1 Hz and the 1^(st) odd harmonicat 3 Hz similarly to the V_(emf) spectrum. This harmonic was not presentduring sine wave excitation and demonstrates the system's ability tosense multi-frequency components within a single temporal event. The oddharmonics were not found in the Δσ spectra for the 2 Hz square wavebecause the low-pass action of the hydrodynamic network acted to filterout these small harmonics. In addition, the 1st odd harmonic (12 Hz) forthe 4 Hz excitation fell outside the bandwidth of EIT acquisition (8.65Hz).

Breast Imaging—Procedure

Women were recruited to be imaged with dynamic EIT, according to method300 (FIG. 3) and using an embodiment of system 100 (FIG. 1), as part ofan Institutional Review Board approved study at the Dartmouth-HitchcockMedical Center (Lebanon, N.H., USA). The imaging procedure is describedin detail in R. J. Halter, A. Hartov, K. D. Paulsen, “A broadband highfrequency electrical impedance tomography system for breast imaging,”IEEE Transactions on Biomedical Engineering, vol. 55, pp. 650-59, 2008which is incorporated by reference herein in its entirety. Briefly, eachwoman was positioned so that one breast hung pendant through an openingin the EIT examination table. The electrodes were actuated to come intocontact with the breast and an effective contact impedance was gauged ateach electrode to ensure that all were in contact with the skin.Conductive gel administered between the electrode and the skin reducedthe level of this contact impedance. A fingerbased pulse-oximetry sensorwas placed on the index or middle finger of the patient and interfacedto an N-395 Pulse Oximeter System (Nellcor Pleasanton, Calif. USA), anembodiment of cardiovascular measurement device 120 (FIG. 1). The devicehad an analog output port that provided a filtered oxygen saturationsignal to the cardiovascular monitoring unit of the EIT system. Thissignal provided triggering for EIT image acquisition and was recordedduring image acquisition so that post-acquisition correlation analysisbetween the cardiovascular and Δσ signals could be evaluated.

In the same way the balloon experiments were conducted, multiple40-frame bursts of EIT voltages were acquired at 17.3 fps. Theindividual bursts were triggered to begin when the pulse-oximetry signalreached a user-specified threshold selected to occur near the apex ofoxygen-saturation during each heartbeat. This threshold was specificallyselected for each patient based on the characteristics of the measuredsignal which varied due to differences in sensor placement, fingerthickness, and other factors. Triggering image acquisition to startprecisely at the peak of the pulse-oximetry signal was not criticalsince a single full heart-beat event was extracted from each data-burstusing method 1200 (FIG. 12).

Following data acquisition (both EIT and pulse-oximetry voltages), Δσimages were reconstructed for each 40-frame burst, method 1200 (FIG. 12)was implemented, and the correlative and spectral power parameters(r_(s), φ(r_(t,max)), r_(t,max), P_(low:full), P_(high:full),P_(low:high)) were extracted for a particular ROI (described below). Theprocedure was performed for both the left and right breast of eachpatient imaged in this study.

The clinical evaluation of the cancer patients participating in thisstudy, included MRI-based tumor identification and localization andbiopsy-based pathological confirmation of disease. Clinical reportsincluded the approximate tumor location (side and clock face) and tumorsize. Because of the approximate nature of the description of tumorlocation and because there was not a one-to-one correspondence betweenMR and EIT imaging, the spatial ROIs selected for analysis were assignedto be all nodes within a particular Δσ image quadrant. Quadrantsdesignated as 1, 2, 3, and 4 corresponded to the area on a clock-facecovered by 12:00-3:00, 3:00-6:00, 6:00-9:00, and 9:00-12:00,respectively. Based on the clinical tumor description, each quadrant wasdesignated as either benign or malignant.

Breast Imaging—Results

Nineteen (19) women were imaged following this protocol (10 with cancer,9 with no cancer). Among the 19 women imaged, there were 13 quadrantsidentified as malignant and 139 designated at benign (152 totalquadrants=19 women×2 sides×4 quadrants).

FIG. 16 shows tumor characteristics for the cancer containing quadrantsin tabular form, wherein RD denotes Radiographic Density, Bx Pathdenotes biopsy based pathological findings, and Enhancement and Kineticsdescribe the washout dynamics of contrast-enhanced MR studies.ED=extremely dense, HD=heterogeneously dense, SC=scattered, DCIS=ductalcarcinoma in situ, IDC=intraductal carcinoma. No MRI was obtained forpatient 9. In three patients (2, 8, and 10), the lesions were identifiedat the 12:00 location and were therefore assigned to both quadrants 1and 4. The temporal Δσ signatures observed in this patient cohortprovided less obvious information than those obtained from the balloonexperiments due to the much smaller changes in Δσ that occurred in vivo.

FIGS. 17A and 17B show exemplary 40-frame Δσ image bursts for a control(normal) and cancer patient, respectively with no obviouslydifferentiating features noted between the two. A 30 mm×30 mm×25 mmtumor was identified in MRI in quadrant 4 of the cancer patient (FIG.17B). The greyscale map represents Δσ and ranges from −5 mS/m to 5 mS/m.

FIG. 18 shows temporal and spectral signatures extracted from a benignquadrant of patient 2 along with associated oxygen-saturation signals.Plots 1810 and 1820 show oxygen-saturation (y) and change inconductivity (Δσ) as a function of time. Plots 1830 and 1840 show powerspectrum for these signals, Y and ΔΣ, respectively. Correlative andpower spectral ratio parameters including r_(s), P_(low:full),P_(high:full), and P_(low:high) are also displayed in FIG. 18.

FIG. 19 shows temporal and spectral signatures extracted from amalignant quadrant of patient 4 along with associated oxygen-saturationsignals. Plots 1910 and 1920 show oxygen-saturation (y) and change inconductivity (Δσ) as a function of time. Plots 1930 and 1940 show powerspectrum for these signals, Y and ΔΣ, respectively. Correlative andpower spectral ratio parameters including r_(s), P_(low:full),P_(high:full), and P_(low:high) are also displayed in FIG. 19.

The correlative and spectral power parameters displayed in FIGS. 18 and19 demonstrate two features that were observed in multiple cases. First,the temporal and spectral correlations between Δσ and theoxygen-saturation signatures are quite low (r<0.09) for the cancerquadrant (FIG. 19), while the benign quadrant (FIG. 18) demonstrates alarger correlation (r>0.24). Second, the spectral traces have moredispersive and dominant low frequency components in the cancercontaining quadrant (FIG. 19). When the quadrants were divided amongstbenign and malignant groups and compared, these two observations wereconsistent and significant.

FIG. 20 shows, in tabular form, statistics of normal and cancer patientobtained from processing of each quadrant of data.

FIG. 21 shows mean parameters for normal and cancer patients. Plots2110, 2120, 2130, 2140, 2150, and 2160 show r_(s), φ(r_(t,max)),r_(t,max), P_(low:full), P_(high:full), P_(low:high), respectively. Ineach of plots 2110, 2120, 2130, 2140, 2150, and 2160, the leftmostcolumn is derived from malignant quadrants and the rightmost column isderived from benign quadrants.

As evident from FIGS. 20 and 21, all parameters were found to besignificantly different (p<0.05) when grouped as malignant and benign,and all but P_(low:high) reached significance levels of p<0.01. Inaddition, no significant differences (p>0.1) were noted between thespectral power ratios of the benign and malignant oxygen-saturationsignals verifying the fact that the cardiovascular signals were similarin both patient cohorts.

FIG. 22 shows receiver-operating characteristics for each of theparameters displayed in FIG. 21.

FIG. 23 shows, in tabular form, clinical metrics associated with thereceiver-operating characteristics displayed in FIG. 22, such as thearea under the curve (AUC) and other relevant clinical metrics includingsensitivity (SN), specificity (SP), accuracy (ACC), positive predictivevalue (PPV), and negative predictive value (NPV). The AUC's of allparameters were greater than 0.67 with r_(t,max) being the bestdiscriminator with an AUC of 0.8, SN of 77%, and SP of 81%. The ratherlow PPV and high NPV are due to the large difference between benign andmalignant sample sizes (139 quadrants vs. 13 quadrants). The thresholdsfor obtaining these levels of SN and SP are also provided in the FIG.23.

Breast Imaging—Discussion

The normal breast is vascularized with a well-organized and regulatednetwork of large feeding arteries and veins coupling into smallerarterioles, capillaries, and venules. The branching pattern is typicallydichotomous as the network extends from the chest wall through thelength of the breast. Around tumors, the vasculature environment issignificantly different. Here, the vasculature is of irregular size,shape, and branching pattern and the network lacks normal hierarchy withhaphazard branching patterns of trifurcated, uneven diameter vasculaturejunctions. Vessel density is higher around the tumor periphery with themean vessel density at the tumor edge being 4-10 times higher than thatinside the tumor. In addition, the individual vessels are compromised,with larger inter-endothelial junctions, increased fenestrations,vesicles and vesico-vascular channels and lack of normal basementmembrane. These features result in lower perfusion rates (blood flow pervolume), lower red blood cell velocity, heterogenous and chaotic bloodflow around the tumor periphery, and a situation in which plasma oozesfrom the tumor periphery into the surrounding normal tissues. Theseabnormalities in blood flow dynamics generate differenttemporally-varying conductivity environments than those associated withthe benign breast.

While the Δσ images observed here (FIGS. 17A and 17B) do not displaywell-resolved blood flow and vasculature patterns, the average valuewithin specific regions of interest do appear to provide signatures thatare significantly different in benign versus malignant breast tissues.The lower correlative parameters (r_(s) and r_(t,max)) for malignantregions potentially arise from the heterogeneous blood flow around andwithin the tumor. In benign vasculature, the blood flow is morehomogenous and more synchronized with the cardiovasculature signaturewhich may explain the somewhat higher correlative parameters observed inthese regions. For both malignant and benign tissues these correlativefactors are only modest (mean r<0.306); this is potentially due to theeffect of averaging a full quadrant of data in the analysis. SmallerROI's will potentially provide areas with higher correlations; however,for the purpose of this study one-to-one correspondence between EIT andclinical MR images was not available and precluded a more refined ROIdefinition.

Both benign and malignant quadrants had the majority of the spectralenergy concentrated in the low frequency band of 1 to 4.325 Hz,similarly to the oxygen-saturation (blood flow) signatures (FIGS. 18 and19). The malignant quadrants, however, had a larger proportion of theenergy in this lower band as compared to the higher frequency band (2.6vs. 1.8, FIG. 20). Leaky vasculature surrounding tumors provides lowresistance pathway for blood to “ooze” into the interstitial spaceswhich may produce more low frequency blood flow signatures than thoseoccurring in uncompromised, more rigid benign vasculature. Further, thelow frequency energy appears to be more dispersive in the malignantquadrants than in the benign quadrants (e.g. see FIGS. 18 and 19). Thisobservation may arise from the extensive microvasculature around tumorswhich has been demonstrated to promote velocity fluctuations that mightexplain the more dispersive spectral content of the blood flowsignatures in the cancer quadrants. The hypotheses formulated from theseobservations require further experimentation to better understand thebiophysical mechanisms producing the effects. Animal models explicitlyevaluating the dynamically changing electrical properties associatedwith pulsatile blood flow through tumor vasculature may provide furtherinsight.

Despite not having a definitive explanation for the significantdifferences observed between benign and malignant blood flow patterns asgauged by EIT, the clinical metrics computed suggest this modality haspotential for differentiating benign from malignant tissues within thebreast. The optimum discriminating parameter, r_(t,max), provides asense of how well the changing conductivity distribution within a regioncorrelates with the periodic blood flow pattern. In benign tissues thisparameter seems to have a higher correlation (˜0.3) compared tomalignant tissues where very little correlation (˜0.09) appears,suggesting that in these regions the heterogeneous flow patternsassociated with malignancy do not follow that of the cardiac-drivenblood flow.

EXAMPLE II: Electrohemodynamic Correlation Imaging in Breast

In this Example, an embodiment of system 900 (FIG. 9) is used to createimages of the spatial correlation of temporally varying electricalproperties of breast tumors to pulsatile oxygen-saturation signatures.This Example utilizes an embodiment of system 900, wherein imagingdevice 110 (FIG. 1) is an EIT imaging device, cardiovascular measurementdevice 120 (FIG. 1) is a pulse-oximeter, and second imaging device 910is an MRI device. Additionally, this example utilizes an embodiment ofmethod 600, wherein step 601 is performed based upon images generated bythe MRI device. Specifically, EIT images were recorded synchronouslywith pulse-oximetry in a series of 18 women (8 with cancer, 10 withoutcancer) at 20 frames per second for time series of 7-10 cardiac cycles.Using an embodiment method 300 (FIG. 3) including step 350, correlationsmaps (embodiments of the correlation-indicating images discussed inconnection with step 350) representing the relationship betweentemporally varying electrical conductivity and pulse-oximetry werecomputed for both breasts of each subject. Significantly lowercorrelations were found in malignant as compared to benign regions, andappear to serve as surrogate measures of tumor hemodynamics likely as aresult of the vastly different vascular patterns in malignancies. Thecorrelation metric provided predictive clinical value and is suggestedas a potential biomarker for use in screening, diagnostic, or therapymonitoring populations.

Results—Patient Population

Bilateral pulse-oximetry gated dynamic electrical conductivity images ofthe breast were acquired from 18 women. Eight of these participants hadbiopsy confirmed carcinoma (American College of Radiology (ACR) BIRADScategory 6), one woman had a biopsy confirmed benign parenchymal lesionnoted on mammography (ACR BIRADS category 4B), and nine women had nomammographic abnormalities (ACR BIRADS category 0).

FIG. 24 shows patient characteristics. Radiographic density categoriesare: extremely dense (ED), heterogeneously dense (HD), and scattereddensity (SD).

FIG. 25 shows abnormal cohort characteristics, wherein IDC refers tointraductal carcinoma, DCIS refers to ductal carcinoma in situ. No MRIwas obtained for patient 9; characteristics for patient 9 are based uponmammographic interpretation only.

Results—Correlation Images of Pulsatile Conductivity

FIGS. 26A-E show a representative sequence of changing conductivity andits correlation with pulsatile oxygen saturation for a woman with apathologically confirmed inflammatory breast cancer in the upper outerquadrant of her right breast. In this case, the change in conductivityranged from −7.9 mS/m to 6.3 mS/m with respect to baseline over thecourse of eight heartbeats, as seen in FIGS. 26A and 26B. Novisually-evident signatures differentiated benign from malignant regionswithin the breast in the raw conductivity images (see FIG. 26A). Thechange in conductivity (Au) at a single image pixel was extracted frombenign and malignant regions at each time instant (see FIGS. 26C and26D). When these dynamic conductivity signatures were phase-shifted andlinearly correlated with the oxygen saturation signal (FIG. 26B),substantial differences in the maximum correlation coefficients(r_(malignant)=0.04 vs. r_(benign)=0.25 for the example shown) wereobserved. The temporal conductivity sequence at each image pixel wasextracted and its correlation with the oxygen saturation signal wascomputed. The correlation coefficient calculated at each pixel was usedto form an image of the correlative power between the pulsatileconductivity and the oxygen saturation signal (see FIG. 26E).

Results—Imaging Women with Breast Cancer

FIGS. 27A-E show results obtained by imaging women with breast cancer.Magnetic resonance (MR) imaging studies were obtained from the ninewomen with mammographically noted abnormalities, and the lesions wereidentified by clock face position and size, as shown in FIG. 27A. Thecorresponding right and left correlation maps were constructed for eachof these cases (FIG. 27B). The mean correlation coefficient computedwithin a user-specified region-of-interest (ROI) surrounding the lesionwas compared to a symmetrically designated ROI in the contralateralbreast. The tumor ROI was manually selected at a region of lowcorrelation in close proximity to the clinically identified position.The mean correlation coefficient of the cancer ROI was less than that inthe symmetric benign ROI in seven of the eight carcinoma cases (FIG.27C) with the benign-to-cancer ratio ranging from 1.9 to 25.8 (mean±SD:9.2±9.1). FIG. 28 shows a correlation coefficient table for the abnormalcohort. The tabulated correlation coefficient represents mean valuewithin the specified ROI. The abnormal ROI of patient 9 has benignbreast parenchyma. The mean correlation coefficient of all cancer ROIswas 0.0337±0.0318 (mean±SD) and for benign ROIs was 0.1103±0.0716(mean±SD) and were significantly different (P=0.0064, test power=0.86)(FIG. 27D). Receiver operating characteristic (ROC) curves wereconstructed to assess the clinical potential for using these metrics todistinguish cancer from benign regions within the breast. The area underthe curve (AUC) for this subset of women with previously identifiedabnormalities was 0.85 (FIG. 27E). At a correlation coefficientthreshold of 0.06321 this metric had a sensitivity (SN) of 0.875,specificity (SP) of 0.8, accuracy (ACC) of 0.833, positive predictivevalue (PPV) of 0.778, and a negative predictive value (NPV) of 0.889.

Results—Comparative Imaging of Women with and without Breast Cancer

FIGS. 29A and 29B show comparative results for women with and withoutbreast cancer. Correlation maps were constructed for the right and leftbreast of each of the 18 women enrolled in this study. Instead ofselecting specific ROIs associated with regions of low correlation, thefour quadrants of each image were selected as a ROI. Based on the MRimaging studies, the quadrants designated at the cardinal locations (NE,SE, SW, NW) were then identified as either malignant or benign. Forthose patients with lesions identified at the 12:00 location, both NWand NE quadrants were designated as malignant (patients 2 and 8, FIG.27A). Of the 144 total quadrants (18 women×two breasts×4 quadrants), 10were identified as malignant and 134 as benign. The mean correlationcoefficient within the malignant quadrants (mean±SD: 0.107±0.067) weresignificantly less than those within the benign quadrants (mean±SD:0.186±0.088) (P=0.0031, test power=0.96) (FIG. 29A). The AUC computedfrom this patient population was 0.769 (FIG. 29B) and the clinicalmetrics obtained at a correlation threshold of 0.118 were 0.8, 0.8, 0.8,0.23, and 0.98 for SN, SP, ACC, PPV, and NPV, respectively.

Methods—Subjects

Women with mammographically noted abnormal breast lesions were asked toparticipate in our IRB-approved study. These women underwentpulse-oximetry gated electrical impedance tomography exams, followed bymammography guided biopsy's and magnetic resonance imaging studies inthe case of positive invasive carcinoma findings on biopsy. Radiographicreview of the MRI produce quantitative measures of lesion size andclock-face location in addition to the qualitative metrics of lesiontype, margin morphology, and contrast enhancement pattern and kinetics.An ACR BIRADS category based on MR and mammographic interpretation wasassigned to each lesion. This group of women composed the abnormalcohort evaluated in this investigation. A second group of women with nonoted abnormal lesions was asked to participate as part of the normal orbenign cohort. This group of women did not undergo biopsy or MRI.

Methods—Electrical Impedance Tomography

Each woman participating in the study was imaged using pulse-oximetrygated dynamic electrical impedance tomography (EIT), the same system asused in Example I. Both the left and right breasts were evaluated duringeach imaging session and a pulse-oximeter sensor was placed on eitherthe index or middle finger of the right hand. Sixteen 1 cm diametercircular Ag/AgCl electrodes arranged in a retractable ring were broughtinto contact with the circumference of the breast, and the imagingprocedure was initiated. The peak of the oxygen saturation signalrecorded through pulse-oximetry was used to trigger EIT dataacquisition. Multiple frames of EIT data were collected at 20 frames persecond and data acquisition occurred over multiple heartbeats (7-10heartbeats). Concurrently with EIT data acquisition, oxygen-saturationwas sampled as a surrogate measure of blood flow. EIT data was collectedat 127 kHz—a frequency where variation between the electricalconductivity of blood and both normal and cancerous breast tissue hasbeen reported. The radial diameter of the electrode array was recordedduring an exam for use in post data acquisition image formation.

Methods—Image Analysis

As discussed in connection with Example I, a linearized finite elementbased numerical algorithm was applied to construct conductivity imagesfor each EIT data frame as described previously. This procedure produceda temporal sequence of conductivity maps corresponding to the sampledoxygen-saturation signal. Linear correlation coefficients were computedbetween the temporal conductivity at a single image location (pixel) andthe recorded oxygen saturation signal, and provided a measure of howwell the conductivity change was linked to the cardiovascular signature.These correlations were calculated for the full range of phase-lags(0-360 degrees) between the signals, and the maximum correlationindependent of the phase-lag was assigned to each image location fromwhich a correlation map was constructed. These maps depict regions wheremore or less correlation occurred between the oxygen saturation signal(pulsatile blood flow) and the change in conductivity. Additionaldetails regarding signal analysis and imaging processing are provided inExample I.

Methods—Statistical Analysis

Within the abnormal group, we manually selected circular regions ofinterest (ROI) in close proximity to MRI-based lesion locations that hadfocal areas of low correlation. The ROI was defined to have a diameterequivalent to the mean of the MRI reported lesion dimensions. A secondsimilarly sized ROI was positioned in the contralateral breast at themirrored location. The mean correlation coefficient within each ROI wascomputed and served as the primary variable for comparison. We performedROC analysis to evaluate the discriminatory power that this correlationmetric provided for differentiating malignant from benign regions.Within the abnormal group, ROIs of the tumor-bearing breasts wereclassified as malignant, while ROIs in the contralateral side werecategorized as benign. We calculated the AUC for these curves anddetermined the classification threshold at which an 80% specificitywould be achieved. With this threshold, we computed the clinicalparameters of sensitivity, accuracy, positive predictive value, andnegative predictive value. In a second analysis, we computed the meancorrelation coefficient from each of the four quadrants comprising acorrelation map. These means were calculated for both breasts of womenin both the abnormal and normal cohorts. Each quadrant was designated asmalignant or benign based on clinical image studies (MR andmammography). Similar ROC analysis was performed to assess thediscriminatory power of these quadrant correlations. From this analysissensitivity, specificity, accuracy, positive predictive values, andnegative predictive values were computed. In both ROI and quadrant-basedanalyses, two-group mean-comparison t-tests were constructed to comparethe benign and malignant groupings. The power of each comparison wasestimated from the computed mean values and standard deviations and thespecific group sample sizes. All statistical computations were performedwith Stata/IC 10.0 (StataCorp LP, College Station, Tex.) and resultswere deemed significant for p<0.01.

Combinations of Features

Features described above as well as those claimed below may be combinedin various ways without departing from the scope hereof. For example, itwill be appreciated that aspects of one cardiovascular-dynamicscorrelated imaging system, or method, described herein may incorporateor swap features of another cardiovascular-dynamics correlated imagingsystem, or method, described herein. The following examples illustratepossible, non-limiting combinations of embodiments described above. Itshould be clear that many other changes and modifications may be made tothe systems and methods described herein without departing from thespirit and scope of this invention:

(A1) A method for cardiovascular-dynamics correlated imaging may includereceiving a time series of images of at least a portion of a patient,receiving a time series of cardiovascular data for the patient, andevaluating correlation between the time series of images and the timeseries of cardiovascular data.

(A2) The method denoted as (A1) may further include determining aproperty of the at least a portion of a patient, based upon thecorrelation between the time series of images and the time series ofcardiovascular data.

(A3) In the method denoted as (A2), the property may be presence ornon-presence of vascularized cancer tissue in the at least a portion ofa patient.

(A4) The method denoted as (A3) may further include, if the step ofdetermining results in determining presence of vascularized cancertissue, diagnosing the patient with tumor angiogenesis.

(A5) In the method denoted as (A2), the property may be amount or typeof vascularized tissue in the at least a portion of a patient.

(A6) The method denoted as (A5) may further include assessingprogression of tumor angiogenesis from the amount or type ofvascularized tissue.

(A7) In any of the methods denoted as (A1) through (A6), the step ofreceiving a time series of images may include receiving a time series ofelectrical impedance tomography images.

(A8) In the method denoted as (A7), the step of receiving a time seriesof electrical impedance tomography images may include receiving a timeseries of single-frequency electrical impedance tomography images.

(A9) In the method denoted as (A7), the step of receiving a time seriesof electrical impedance tomography images may include receiving a timeseries of multi-frequency electrical impedance tomography images.

(A10) In any of the methods denoted as (A1) through (A6), in the step ofreceiving a time series of images, the time series of images may beselected from the group consisting of a time series of ultrasoundimages, a time series of video endoscopy images, and a time series offluoroscopy images.

(A11) In any of the methods denoted as (A1) through (A6), in the step ofreceiving a time series of images, the time series of images may beselected from the group consisting of a time series of magneticresonance images and a time series of computed tomography images.

(A12) In any of the methods denoted as (A1) through (A11), the timeseries of images may define a plurality of time series of images, andeach of the plurality of time series of images may be gated by the timeseries of cardiovascular data to a different respective phase relativeto the time series of cardiovascular data.

(A13) In any of the methods denoted as (A1) through (A12), the step ofreceiving a time series of cardiovascular data may include receiving atime series of pulse-oximetry data.

(A14) In any of the methods denoted as (A1) through (A12), in the stepof receiving a time series of cardiovascular data, the time series ofcardiovascular data may be selected from a time series ofelectrocardiogram data, a time series of arterial pressure data, and atime series of blood flow data.

(A15) In any of the methods denoted as (A1) through (A14), the step ofevaluating correlation may include evaluating at least one of spectralcorrelation and temporal correlation between the time series of imagesand the time series of cardiovascular data.

(A16) In any of the methods denoted as (A1) through (A15), the step ofevaluating correlation may include analyzing correlation between thetime series of images and heartbeat cycles identified from the timeseries of cardiovascular data.

(A17) In the method denoted as (A16), the step of evaluating correlationmay include (a) detecting at least one cardiovascular signature in thetime series of cardiovascular data to identify at least one heartbeatcycle, (b) referencing the time series of images to timing of the atleast one heartbeat cycle, and (c) analyzing the time series of imagesas a function of timing within a heartbeat cycle.

(A18) In the method denoted as (A17), the step of referencing mayinclude, if the heartbeat rate is not constant, resampling the timeseries of cardiovascular data, and in accordance therewith the timeseries of images, to emulate a constant heartbeat rate.

(A19) In any of the methods denoted as (A16) through (A18), the step ofanalyzing correlation may include (a) extracting, from the time seriesof images, a respective time series of position sensitive signals eachrepresenting same spatial region of interest of the time series ofimages, and (b) analyzing the time series of position sensitive signalsas a function of timing within heartbeat cycles.

(A20) In the method denoted as (A19), the step of analyzing the timeseries of position sensitive signals may include determining one or moreparameters indicative of correlation between the time series of positionsensitive signals and the time series of cardiovascular data, whereinthe time series of signals and the time series of cardiovascular dataare referenced to the heartbeat cycles.

(A21) In the method denoted as (A20), the step of analyzing the timeseries of position sensitive signals may further include combining twoor more of the parameters indicative of correlation to assess degree ofcorrelation.

(A22) In any of the methods denoted as (A20) and (A21), the step ofdetermining one or more parameters indicative of correlation may includecalculating at least one of spectral correlation coefficient and maximumtemporal correlation coefficient.

(A23) In the method denoted as (A22), the step of calculating maximumtemporal correlation coefficient may include (a) calculating thetemporal correlation coefficient as a function of phase shift betweenthe time series of position sensitive signals and the time series ofcardiovascular data, and (b) determining the maximum value of thetemporal correlation coefficient as a function of the phase shift.

(A24) In any of the methods denoted as (A20) and (A21), the step ofdetermining one or more parameters indicative of correlation may include(a) calculating the temporal correlation coefficient as a function ofphase shift between the time series of position sensitive signals andthe time series of cardiovascular data, and (b) determining the phaseshift at which the temporal correlation coefficient as a function of thephase shift attains maximum value.

(A25) In any of the methods denoted as (A20) and (A21), the step ofdetermining one or more parameters indicative of correlation may includeat least one of (a) comparing temporal distributions of the time seriesof position sensitive signals and the time series of cardiovascular dataand (b) comparing spectral distributions of the time series of positionsensitive signals and the time series of cardiovascular data.

(A26) In any of the methods denoted as (A19) through (A25), the step ofanalyzing the time series of position sensitive signals may includeevaluating spectral distribution of the time series of positionsensitive signals as a function of timing within heartbeat cycles.

(A27) In the method denoted as (A26), the step of evaluating spectraldistribution may include determining one or more spectral power ratiosof the time series of position sensitive signals.

(A28) Any of the methods denoted as (A19) through (A27) may furtherinclude selecting the spatial region of interest.

(A29) In any of the methods denoted as (A1) through (A28), the timeseries of images may be a time series of first-type images, and themethod may further include overlaying at least a portion of the timeseries of first-type images on a time series of second-type images of asecond portion of the patient, the second-type images being of typedifferent from the first-type images.

(A30) In the method denoted as (A29), the first-type images may beelectrical impedance tomography images and the second-type images may beselected from the group consisting of ultrasound images, magneticresonance images, positron emission tomography images, and computedtomography images.

(A31) In any of the methods denoted as (A29) and (A30), the step ofoverlaying may include overlaying, with the time series of second-typeimages, a portion of the time series of first-type images meetingcriteria of correlation with the time series of cardiovascular data.

(A32) Any of the methods denoted as (A1) through (A31) may furtherinclude generating at least one correlation-indicating image indicatingcorrelation between the time series of images and the time series ofcardiovascular data.

(A33) The method denoted as (A32) may further include overlaying the atleast one correlation-indicating image on the time series of images.

(A34) In any of the methods denoted as (A32) and (A33), the time seriesof images may be a time series of first-type images, and the method mayfurther include (a) generating a time series of second-type images of asecond portion of the patient having a spatial overlap with the at leasta portion of a patient, wherein the second-type images are of typedifferent from the first-type images, and (b) overlaying the at leastone correlation-indicating image on the time series of second-typeimages.

(A35) In the method denoted as (A34), the step of generating a timeseries of second-type images may be performed concurrently with the stepof generating the time series of images.

(A36) In each of the methods denoted as (A1) through (A33), the timeseries of images may be a time series of a first-type images and themethod may further include (a) receiving at least one second-type imageof a second portion of the patient having a spatial overlap with the atleast a portion of a patient, and (b) using the at least one second-typeimage to determine a spatial region of interest of the first time seriesof first-type images to be considered in the step of evaluatingcorrelation.

(A37) In the method denoted as (A36), the first-type images may beelectrical impedance tomography images and the second-type images may beselected from the group consisting of ultrasound images, magneticresonance images, and computed tomography images.

(A38) Any of the methods denoted as (A1) through (A37) may furtherinclude generating the time series of images, and recording the timeseries of cardiovascular data.

(A39) In the method denoted as (A38), the step of generating a timeseries of images may include generating a time series of images at afirst series of times, the step of recording a time series ofcardiovascular data may include recording a time series ofcardiovascular data at a second series of times, and the step ofevaluating correlation may include resampling at least one of the timeseries of images and the time series of cardiovascular data tosynchronize the first series of times and the second series of times.

(A40) In any of the methods denoted as (A38) and (A39), the step ofgenerating the time series of images may include generating a timeseries of electrical impedance tomography images.

(A41) In the method denoted as (A40), the step of generating a timeseries of electrical impedance tomography image may include, for eachelectrical impedance tomography image, (a) measuring a plurality ofvoltages or currents at a respective plurality of spatially separatedelectrodes in contact with the patient, and (b) reconstructing theelectrical impedance tomography image from the plurality of voltages,wherein the electrical impedance tomography image is a spatialdistribution of change of an electrical impedance related property ascompared to a reference distribution of the electrical impedance relatedproperty.

(A42) In the method denoted as (A41), the step of reconstructing mayinclude applying a finite element based linear difference algorithm.

(B1) A system for cardiovascular-dynamics correlated imaging may includea processing device having (a) a processor, (b) a memory communicativelycoupled with the processor, and (c) a correlation module includingmachine-readable instructions stored in the memory that, when executedby the processor, perform the function of correlating a time series ofimages of at least a portion of a patient with a time series ofcardiovascular data of the patient to determine a property of the atleast a portion of a patient.

(B2) The system denoted as (B1) may further include an imaging devicefor generating at least one of (a) the time series of images and (b)data from which the time series of images can be reconstructed.

(B3) In the system denoted as (B2), the imaging device may be anelectrical impedance tomography device.

(B4) In the system denoted as (B2), the imaging device may be selectedfrom the group consisting of an ultrasonograph, a video endoscope, afluoroscope, a magnetic resonance scanner, and a computed tomographyscanner.

(B5) Any of the systems denoted as (B1) through (B4) may further includea cardiovascular measurement device for recording the time series ofcardiovascular data.

(B6) In the system denoted as (B5), the cardiovascular measurementdevice may be a pulse oximeter.

(B7) In the system denoted as (B5), the cardiovascular measurementdevice may be selected from the group consisting of anelectrocardiograph, a sphygmomanometer, and a blood flow measurementdevice.

(B8) Any of the systems denoted as (B1) through (B7) may further includean image reconstruction module having machine-readable instructionsstored in the memory that, when executed by the processor, perform thefunction of reconstructing the time series of images from a time seriesof position sensitive data.

(B9) In the system denoted as (B8), the image reconstruction module mayinclude machine-readable instructions stored in the memory that, whenexecuted by the processor, perform the function of reconstructing thetime series of images from the time series of position sensitive datausing at least one of a finite element based linear differencealgorithm, a boundary element method, and back projection.

(B10) In the system denoted as (B8), the image reconstruction module mayinclude machine-readable instructions stored in the memory that, whenexecuted by the processor, perform the function of reconstructing a timeseries of electrical impedance tomography images from a time series ofposition sensitive voltage measurements obtained using electrodes incontact with the at least a portion of a patient, wherein the timeseries of electrical impedance tomography images is a spatialdistribution of electrical conductivity change as compared to areference electrical conductivity distribution.

(B11) Any of the systems denoted as (B1) through (B10) may furtherinclude a resampling module including machine-readable instructionsstored in the memory that, when executed by the processor, perform thefunction of resampling a time series of data from a series of times, atwhich the time series of data is generated, to a desired series oftimes.

(B12) In the system denoted as (B11), the resampling module may furtherinclude machine-readable instructions stored in the memory that, whenexecuted by the processor, perform the function of resampling, if thetime series of images and the time series of cardiovascular data areassociated with different series of times, at least one of the timeseries of images and the time series of cardiovascular data tosynchronize the time series of images and the time series ofcardiovascular data.

(B13) Any of the systems denoted as (B1) through (B12) may furtherinclude a signature identification module with machine-readableinstructions stored in the memory that, when executed by the processor,perform the function of identifying a time series of cardiovascularsignatures in the time series of cardiovascular data.

(B14) In the system denoted as (B13), the signature identificationmodule may further include machine-readable instructions stored in thememory that, when executed by the processor, perform the functionidentifying heartbeat cycles from the time series of cardiovascularsignatures.

(B15) The system denoted as (B14) may further include a resamplingmodule with machine-readable instructions stored in the memory that,when executed by the processor, perform the function of resampling, ifheartbeat rate of patient is not constant, the time series of images andthe time series of cardiovascular data to produce a heartbeat rateregularized time series of images and a heartbeat regularized timeseries of cardiovascular data, wherein the heartbeat rate regularizedtime series of images and the heartbeat regularized time series ofcardiovascular data emulate a constant heartbeat rate.

(B16) In any of the systems denoted as (B1) through (B16), thecorrelation module may include machine-readable instructions stored inthe memory that, when executed by the processor, perform the function of(a) extracting, from the time series of images, a respective time seriesof position sensitive signals each representing same spatial portion ofthe time series of images, and (b) analyzing the time series of positionsensitive signals as a function of timing within heartbeat cycles.

(B17) In the system denoted as (B16), the correlation module may includemachine-readable instructions stored in the memory that, when executedby the processor, perform the function of evaluating spectraldistribution of the time series of position sensitive signals as afunction of timing within heartbeat cycles.

(B18) In the system denoted as (B17), the correlation module may includemachine-readable instructions stored in the memory that, when executedby the processor, perform the function of evaluating at least one ofspectral correlation and temporal correlation between the time series ofimages and the time series of cardiovascular data.

(B19) In any of the systems denoted as (B1) through (B18), thecorrelation module may include machine-readable instructions stored inthe memory that, when executed by the processor, perform the function ofcalculating at least one parameter indicative of correlation between thetime series of images and the time series of cardiovascular data.

(B20) In the system denoted as (B19), the at least one parameter may beselected from the group consisting of spectral correlation coefficient,maximum temporal correlation coefficient when varying phase between thetime series of images and the time series of cardiovascular data, andphase at which maximum temporal correlation coefficient is attained.

(B21) Any of the systems denoted as (B1) through (B20) may furtherinclude an overlay module including machine-readable instructions storedin the memory that, when executed by the processor, perform the functionof overlaying a time series of correlation-indicating images indicativeof correlation between the time series of images and the time series ofcardiovascular data on at least one second image of type different fromthe images of the time series of images.

(B22) In the system denoted as (B21), the at least one second image maybe a time series of second images.

Changes may be made in the above systems and methods without departingfrom the scope hereof. It should thus be noted that the matter containedin the above description and shown in the accompanying drawings shouldbe interpreted as illustrative and not in a limiting sense. Thefollowing claims are intended to cover generic and specific featuresdescribed herein, as well as all statements of the scope of the presentsystem and method, which, as a matter of language, might be said to falltherebetween.

What is claimed is:
 1. A method for cardiovascular-dynamics correlatedimaging, comprising: receiving a time series of images of one or moreregions of a patient; receiving a time series of cardiovascular data forthe patient, the time series of cardiovascular data being recordedconcurrently with the time series of images; evaluating correlationbetween temporal variation in the time series of images and temporalvariation in the time series of cardiovascular data, the correlationincluding at least one of temporal correlation and spectral correlation;identifying, in the images, vascularized tissue of the patient basedupon the correlation; and determining a property of the patient,relating to the vascularized tissue, based upon the correlation.
 2. Themethod of claim 1, the step of receiving a time series of imagescomprising receiving a time series of electrical impedance tomographyimages.
 3. The method of claim 1, further comprising generating the timeseries of images as a time series of electrical impedance tomographyimages.
 4. The method of claim 1, the step of receiving a time series ofcardiovascular data comprising receiving a time series of pulse-oximetrydata.
 5. The method of claim 1, in the step of evaluating correlation,the temporal variation being temporal variation in heartbeat cyclesidentified from the time series of cardiovascular data.
 6. The method ofclaim 5, the step of evaluating correlation comprising: detecting atleast one cardiovascular signature in the time series of cardiovasculardata to identify at least one heartbeat cycle; referencing the timeseries of images to timing of the at least one heartbeat cycle, saidreferencing comprising, if heartbeat rate is not constant, resamplingthe time series of cardiovascular data, and in accordance therewith thetime series of images, to emulate a constant heartbeat rate; andanalyzing the time series of images as a function of timing within aheartbeat cycle.
 7. The method of claim 5, the step of evaluatingcorrelation comprising: extracting, from the time series of images, arespective time series of position sensitive signals each representingsame spatial region of interest of the time series of images; anddetermining one or more parameters indicative of correlation betweentemporal variation in the time series of position sensitive signals andthe temporal variation in the time series of cardiovascular data, thetime series of signals and the time series of cardiovascular data beingreferenced to the heartbeat cycles.
 8. The method of claim 7, the stepof determining one or more parameters indicative of correlationcomprising: calculating temporal correlation coefficient as a functionof phase shift between the time series of position sensitive signals andthe time series of cardiovascular data; and determining maximum value ofthe temporal correlation coefficient as a function of the phase shift.9. The method of claim 7, the step of determining one or more parametersindicative of correlation comprising: calculating temporal correlationcoefficient as a function of phase shift between the time series ofposition sensitive signals and the time series of cardiovascular data;and determining phase shift at which the temporal correlationcoefficient as a function of the phase shift attains maximum value. 10.The method of claim 7, the step of determining one or more parametersindicative of correlation comprising at least one of (a) comparingtemporal distributions of the time series of position sensitive signalsand the time series of cardiovascular data and (b) comparing spectraldistributions of the time series of position sensitive signals and thetime series of cardiovascular data.
 11. The method of claim 5, the stepof analyzing correlation comprising: extracting, from the time series ofimages, a respective time series of position sensitive signals eachrepresenting same spatial region of interest of the time series ofimages; and evaluating spectral distribution of the time series ofposition sensitive signals as a function of timing within heartbeatcycles.
 12. The method of claim 1, further comprising generating atleast one correlation-indicating image indicating the correlation. 13.The method of claim 12, further comprising overlaying the at least onecorrelation-indicating image on the time series of images.
 14. Themethod of claim 1, the property being presence or non-presence ofvascularized cancer tissue in the one or more regions of the patient.15. The method of claim 1, the property being amount or type ofvascularized tissue in the one or more regions of the patient.